Fine-Tuning vs Distillation vs Prompt Engineering for LLMs in Pharma and Life Sciences

Introduction

Large Language Models (LLMs) are increasingly used in the pharmaceutical and life sciences industries for tasks ranging from drug discovery to clinical report summarization (Using large language models for safety-related table summarization in… - Sheraz Khan) (Applications of LLMs in the Pharmaceutical Industry - by Juan Martinez - MantisNLP - Medium). Data scientists have several strategies to adapt these models for specialized applications: fine-tuning (further training the model on domain-specific data), distillation (compressing a model into a smaller one), and prompt engineering (crafting inputs to guide the model without changing its parameters). This article provides an in-depth comparison of these approaches – their methodologies, advantages, drawbacks, and use cases in pharma and life sciences – with a technical focus and real-world examples.

Fine-Tuning LLMs for Domain Expertise

What it is: Fine-tuning is the process of taking a pre-trained LLM and further training it on a custom dataset to specialize it for a specific domain or task (deepset Blog - Fine-tuning Large Language Models). Instead of training a new model from scratch, fine-tuning leverages the rich linguistic knowledge already learned by a foundation model and adapts it to new data. For example, a general model like GPT-3 can be fine-tuned on a corpus of biomedical literature so that it better understands pharmaceutical terminology and tasks. Fine-tuning updates the model's weights through additional backpropagation on the new data, essentially filling the gap between general knowledge and domain-specific expertise (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). Modern fine-tuning can be full-model or "parameter-efficient" (updating only parts of the network, e.g. using LoRA or adapters (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers)) to reduce computational cost. Fine-tuning was crucial in creating instruction-following chatbots (like ChatGPT) from base models, and it remains a key step to achieve reliable task performance in specialized domains (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health).

Advantages: Fine-tuning offers several benefits for pharma and medical applications:

Domain-specific knowledge & accuracy: A fine-tuned model can incorporate terminology and facts from biomedical data not present in the original training. General-purpose LLMs often lack sufficient knowledge of niche biomedical topics; fine-tuning infuses domain-specific expertise for tasks like health report summarization or adverse event detection (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). This often leads to improved performance on specialized tasks, while still preserving the general language understanding of the base model (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). In practice, fine-tuned LLMs have achieved impressive accuracy on medical benchmarks. For example, Google's Med-PaLM 2 (a fine-tuned version of PaLM-2 on medical Q&A data) reached about 85–86% accuracy on USMLE and other medical exam questions – comparable to expert physicians (Google's Med-PaLM 2: A Paradigm Shift in Medical Education and Examination Preparation). Such results underscore that fine-tuning can unlock expert-level performance in a biomedical domain.
Data efficiency: Fine-tuning can succeed with relatively small labeled datasets, since the model's prior knowledge reduces the amount of new data needed (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). A few hundred or thousand domain-specific examples may suffice to significantly improve the model's outputs (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). This is especially valuable in life sciences, where curated labeled data (e.g. clinical trial outcomes) can be limited.
Resource efficiency vs. training from scratch: Adapting an existing LLM is far cheaper and faster than building a new model. Fine-tuning uses far less compute and time than training a billion-parameter model from the ground up (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). This makes it practical for pharmaceutical companies to obtain a custom model on their data without incurring the enormous cost of full training.
Task specialization: Through fine-tuning, an LLM can be optimized for specific tasks such as classification of medical texts, named-entity recognition (e.g. identifying genes or chemicals in text), or translation of clinical notes (deepset Blog - Fine-tuning Large Language Models). The model learns to prioritize patterns relevant to the task, improving accuracy on that task. (However, this narrow focus can come with an "alignment tax" of slightly reduced generality (deepset Blog - Fine-tuning Large Language Models).)

Disadvantages and Challenges: Despite its power, fine-tuning has important limitations, especially in regulated industries:

Cost and Maintenance: Fine-tuning every time data or requirements change can be costly. In a dynamic domain (new research, evolving data), a fine-tuned model can become outdated and require periodic re-training (deepset Blog - Fine-tuning Large Language Models). This ongoing maintenance adds to cost and effort, on top of the initial fine-tuning expense for computing and data preparation. Furthermore, if using a third-party LLM provider, fine-tuning can be more expensive per query than using the base model (deepset Blog - Fine-tuning Large Language Models).
Hallucinations remain: Fine-tuning does not eliminate the inherent tendency of LLMs to "hallucinate" incorrect information (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). A model might confidently generate a nonexistent drug interaction or a false clinical detail, even after fine-tuning. Domain-specific fine-tuning can mitigate this by teaching the model more factual info, but it's not a guarantee – careful validation is still required for any generated content.
Compliance and consistency: In pharmaceutical applications, factual accuracy and exact phrasing can be critical (for example, regulatory or safety statements must be precise). Fine-tuned models produce probabilistic outputs, so they may paraphrase or slightly vary approved wording (Ostro - Why Fine Tuning is Not Fine for Pharma Marketing). This nondeterminism poses a compliance risk – even a well-tuned model might occasionally generate an output that deviates from required medical terminology or approved claims. Such variability is unacceptable in scenarios like generating patient medication guides, where any deviation from the approved text is a compliance violation (Ostro - Why Fine Tuning is Not Fine for Pharma Marketing). Ensuring absolute consistency would require additional guardrails beyond just fine-tuning.
Data and security constraints: Fine-tuning an LLM requires access to domain data, which in medicine often includes sensitive or proprietary information (patient records, trial data). Organizations may be unwilling or legally unable to send such data to an external cloud service for fine-tuning. While open-source models can be fine-tuned on-premises, not all companies have the infrastructure. Even when possible, there are privacy safeguards needed – e.g. de-identification of patient data – before fine-tuning (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). If using a cloud API, there's also the concern that the fine-tuned model (which now encodes some proprietary data in its weights) resides on a third-party server (deepset Blog - Fine-tuning Large Language Models). These security and privacy issues make some firms prefer alternative methods that keep data local.
Limited by data quality: Fine-tuning is only as good as the data provided. In life sciences, curating a high-quality fine-tuning dataset is a non-trivial effort. If the domain data is sparse or noisy, the fine-tuned model might overfit or learn biases. For highly novel tasks, there simply may not be enough labeled data to fine-tune effectively (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health) (for instance, a very rare disease with few documented cases). In such cases, prompt engineering or retrieval-based methods might be more feasible than fine-tuning.
Bias and ethical concerns: A fine-tuned model can inherit or even amplify biases present in the domain-specific data (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). In healthcare, this is critical – if the training data underrepresents certain patient groups, the model's outputs could inadvertently reinforce disparities. Ongoing monitoring and techniques to mitigate bias (including careful prompt design or algorithmic debiasing) are needed when fine-tuning on medical data (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health).

Use Cases in Pharma/Life Sciences: Fine-tuning has been successfully applied in many biomedical NLP scenarios. For example, researchers fine-tuned a large model to summarize radiology reports in a structured way (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models), tailoring the LLM to the highly specialized language of radiologists. Domain-specific LLMs like BioGPT (from Microsoft) were created by pre-training on biomedical text and then fine-tuning for tasks like question answering and name entity recognition (BioGPT: Generative Pre-trained Transformer for Biomedical Text ...). In drug discovery, fine-tuned models help in text mining patents or scientific literature: a "Biomedical DistilBERT" model (a distilled BERT fine-tuned on PubMed articles) improved accuracy in classifying pharmaceutical patents, while being efficient to run (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC) (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC). These examples show fine-tuning can yield high-performing LLMs for tasks such as medical Q&A, literature review, and document classification. Indeed, Med-PaLM 2's expert-level exam performance demonstrates the potential – a general model became a specialist via fine-tuning (Google's Med-PaLM 2: A Paradigm Shift in Medical Education and Examination Preparation). However, organizations must weigh the accuracy gains against the effort and governance required. In practice, fine-tuning is often chosen when maximum task performance is needed and sufficient domain data is available, such as building an in-house model to analyze clinical trial data with the nuance that a generic LLM lacks.

LLM Distillation for Efficiency and Deployment

What it is: Knowledge distillation is a technique to compress a large model into a smaller one by training the smaller model (student) to imitate the large model (teacher) (LLM distillation demystified: a complete guide - Snorkel AI). In the context of LLMs, distillation means using a powerful, huge model's behavior to train a lightweight model that is easier to deploy. The key idea, originally introduced by Hinton et al., is that the large model's outputs (or intermediate representations) contain informative soft targets that the small model can learn from. By preserving the teacher's knowledge, the student aims to achieve similar performance on specific tasks with much fewer parameters (LLM distillation demystified: a complete guide - Snorkel AI). For example, one could use GPT-4 (teacher) to generate answers to thousands of medical questions, then train a smaller model on those question-answer pairs so that it learns to produce GPT-4-like answers. The result is a distilled model specialized on that task. Distillation can also be applied by fine-tuning a smaller open-source model on the outputs of a larger model (this approach was used in creating instruction-following models like Alpaca). The outcome of LLM distillation is a model that is much smaller and faster than the original, while retaining its task-specific competence.

Advantages: The primary motivation for distillation is to address the practicality of using LLMs in real-world applications:

Efficiency (Speed & Cost): Large LLMs with tens of billions of parameters are resource-intensive – they require powerful GPUs and have high latency and cost per query. A distilled model with, say, one-tenth the parameters can run much faster and cheaper, making it feasible to serve at scale or on modest hardware (LLM distillation demystified: a complete guide - Snorkel AI) (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). For instance, DistilBERT (a distilled version of BERT) retains ~97% of BERT's language understanding performance but is about 40% smaller and 60% faster (BERT based clinical knowledge extraction for biomedical ...). In pharma applications where response time matters (e.g. an interactive clinician support tool) or where many queries must be run, this speedup is crucial. Smaller models also consume less memory and energy, which is beneficial for deploying AI within hospital IT environments or edge devices.
Lower Infrastructure Requirements: Deploying a private large model (like a 175B parameter GPT) within an enterprise, behind firewalls, is often infeasible due to hardware constraints. Distillation produces a compact model that can be hosted on accessible infrastructure (possibly even on CPU servers or mobile devices) (LLM distillation demystified: a complete guide - Snorkel AI). This enables on-premises or offline use of LLM capabilities, which is appealing for sensitive healthcare data. A distilled model is easier to containerize and integrate into existing systems without specialized AI hardware.
Task-specific optimization without full training: Distillation can be seen as an automated way to specialize a model for a task using another model's knowledge. It is especially useful when you have a high-performing teacher model via an API (for example, a proprietary model) but you need a standalone model for deployment. Rather than fine-tuning from scratch (which might require labeled data), you leverage the teacher to generate training data or signals. This can jumpstart development of a model with relatively little human-labeled data (LLM distillation demystified: a complete guide - Snorkel AI). In life sciences, one might distill a powerful text-mining model into a smaller one that can live within a clinical database system to flag safety signals, thereby combining accuracy with convenience.
Maintaining privacy (in some setups): If done correctly, distillation can allow transferring knowledge without directly exposing sensitive data. For example, an organization could query a large model with in-house data (without revealing the data if the model is local or if queries are safe), then use those results to train a small model internally. The smaller model can then be used internally with no external calls, reducing ongoing privacy risk. (Note however, initial use of data with the teacher model must be done carefully to not leak information if the teacher is a third-party API – see drawbacks below.)

Drawbacks: While powerful, distillation has its own challenges to consider:

Potential performance drop: A distilled model usually cannot match the full performance of the original large model (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). There is an inherent trade-off: compressing the knowledge into fewer parameters means some nuances or capabilities may be lost (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). In practice, the student often achieves close but slightly lower accuracy on the specific task. For example, a distilled toxicity classifier might be faster but a bit less precise than the original LLM-based classifier. This gap may or may not be critical depending on the application; in sensitive domains, even a few percentage points of accuracy matter. It's often observed that "LLMs with more parameters almost always generate better predictions than those with fewer" (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers), so distillation trades raw quality for efficiency.
Task limitation (narrower scope): The simplest form of distillation trains the student to emulate the teacher on a given task or dataset (LLM distillation demystified: a complete guide - Snorkel AI). This means the distilled model is usually narrowly specialized. Unlike the teacher, which might be a general-purpose model, the student might not generalize well beyond the distilled task. In other words, if you distilled an LLM to answer chemistry questions, that student model may perform poorly if asked an out-of-scope question about finance. The student is effectively limited by the teacher's knowledge on the distilled task and often has less flexibility (LLM distillation demystified: a complete guide - Snorkel AI). For broader use, you would need to distill multiple abilities or accept reduced versatility.
Data requirements: Distillation often requires large amounts of unlabeled or pseudo-labeled data to succeed (LLM distillation demystified: a complete guide - Snorkel AI). You need to run the teacher on many examples to transfer its knowledge. In pharma, getting a diverse and representative set of prompts for the teacher model (for example, all kinds of questions a medical assistant might get) can be difficult. If the domain data is scarce, the distillation won't capture the full behavior. Generating a synthetic dataset via the teacher is a common approach, but it needs careful curation to cover the desired scope.
Licensing and usage constraints: A subtle issue is that not all model providers allow unrestricted use of their outputs for distillation. Many proprietary LLM APIs forbid using the model's responses to train a competing model (LLM distillation demystified: a complete guide - Snorkel AI). For instance, OpenAI's terms might disallow feeding ChatGPT outputs into your own model training. This can legally limit the use of certain "teachers." Thus organizations often rely on open-source or internally trained large models for the teacher to avoid TOS violations.
Need for expertise to tune the process: Achieving good distillation is somewhat of an art. It may require trying advanced techniques (e.g., distilling intermediate layers, or using multiple teachers and voting to improve student labels (LLM distillation demystified: a complete guide - Snorkel AI)) to get the student model's performance up. In one case study, simply distilling labels from a single LLM yielded a baseline F1 score of 50%, but engineers improved it by prompt engineering the teacher and cleaning the distilled data, and even then needed additional methods to reach production-level accuracy (LLM distillation demystified: a complete guide - Snorkel AI) (LLM distillation demystified: a complete guide - Snorkel AI). This indicates that while distillation can jumpstart a model, it might still require significant work (and possibly some human-labeled data) to refine the student.
Retention of errors and biases: The student model will learn not only the teacher's strengths but also its flaws. If the teacher LLM sometimes produces hallucinations or reflects biases in its outputs, those can be passed on to the student through the distilled training data. For example, if a teacher occasionally gives a misleading answer about a drug's effect, the student will likely mimic that. Distillation doesn't inherently fix errors; it can even amplify them if the student generalizes incorrectly from imperfect teacher outputs. Thus, careful oversight of the generated training data (or filtering of teacher mistakes) is important when distilling for high-stakes domains like healthcare.

Applications in Pharma and Life Sciences: Knowledge distillation is attractive for pharma AI when deployment constraints are strict. A notable use case is compressing large transformer models for on-device or edge use. For instance, a hospital might distill a large medical language model into a smaller one that can run on a secure local server to assist doctors with recommendations in real-time, without relying on an internet connection. We've seen DistilBERT and similar distilled models used in biomedical text mining – one study used a Biomedical DistilBERT model (a version of DistilBERT further trained on biomedical text) to classify drug patents, achieving >93% accuracy with significantly lower runtime, whereas the full BERT model would have been slower (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC) (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC). Distillation is also used in multi-step pipelines: for example, using a large LLM to label a dataset of clinical narratives for presence of adverse events, then distilling a smaller classifier that can quickly scan millions of records. Another emerging scenario is creating specialized chatbots for pharma companies – a company could fine-tune a large model on its proprietary knowledge base and then distill it to a smaller model that can be shipped in an app provided to physicians. This way, the device-running model is efficient and contains the gist of the knowledge without exposing the full large model. In summary, distillation is chosen when organizations need faster, lighter models for deployment but still want to leverage the intelligence of state-of-the-art LLMs. It pairs well with fine-tuning: for example, fine-tune a model on domain data, then distill it to a smaller footprint for production use. The cost is a slight sacrifice in accuracy for big gains in speed and manageability (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers).

Prompt Engineering for LLMs in Pharma

What it is: Prompt engineering is the practice of designing and refining the input prompts or queries given to an LLM in order to elicit the desired output, without altering the model's parameters (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). In essence, it means programming the model through language. Because modern LLMs are so flexible, how you ask your question can dramatically affect the answer quality. Prompt engineering involves creating input text that provides context, instructions, and format guidelines so that the model understands the task correctly and produces a useful, accurate result (Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons) (Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons). This can include specifying the role ("You are an expert chemist…"), giving examples (few-shot prompting), or breaking down a problem (e.g. chain-of-thought prompts). Unlike fine-tuning or distillation, prompt engineering does not require any model training; the same pre-trained LLM is used as-is. Instead, the "engineering" happens on the input side: figuring out the right phrasing or strategy to get the model to leverage its existing knowledge for your task. Prompt engineering has become an important skill in itself, especially in domains like medicine where you must ask questions carefully to get factual and unbiased answers (Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial - PMC).

Advantages: For organizations in pharma and biotech, prompt engineering offers several appealing benefits:

No training needed (instant deployment): Because it uses the model as-is, prompt engineering allows rapid development of LLM-driven solutions. One can achieve results without collecting training data or fine-tuning the model (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). This dramatically lowers the barrier to entry – you can take an API like GPT-4 or an open model and start querying it on your texts immediately. For example, to create a proof-of-concept that summarizes clinical trial protocols, a data scientist could write a prompt that injects the protocol text and asks for a summary, without any model modifications. This makes prompt engineering very cost-effective and quick to iterate, since you avoid the compute and time of model training (Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons). In scenarios where each project is small or experimental, prompt engineering is often the only viable approach due to limited resources.
Flexibility and reuse of a single model: Prompt engineering lets you adapt the same general LLM to many different tasks by just changing the prompt, rather than maintaining separate fine-tuned models (Prompt Engineering vs. Fine-Tuning—Key Considerations and Best Practices - Nexla). This is valuable in life sciences research environments where you might one day query literature for gene-disease relations, and the next day draft a patient-friendly explanation of a condition. Using a versatile foundation model (like GPT-4 or an internally deployed large model) with tailored prompts for each task means you don't have to train a new model for each query type. It's easy to switch tasks: for sentiment analysis vs. summarization vs. translation, you simply provide a different prompt or instructions. This adaptability accelerates development across various use cases.
Preserves model's broad knowledge: Because we are not restricting the model via fine-tuning to a narrow dataset, the LLM retains its wide-ranging knowledge base. This can be useful in pharma where a question might touch on multiple domains. For example, a prompt might ask: "Compare the mechanisms of Drug A and Drug B and their relevance to diabetes treatment." A properly engineered prompt can tap into the model's knowledge of pharmacology, biology, and clinical practice all at once. In contrast, a model fine-tuned only on diabetes data might lack information about one of the drugs if it wasn't in that data. Prompting leverages the full breadth of the pre-trained model's knowledge (up to its training cutoff).
No new risk of data leakage or model security issues: Since prompt engineering does not involve feeding the model new weights or saving proprietary data inside the model, it can be safer in some respects. All proprietary data stays in inputs, which can be logged and controlled. If using an internal model, prompt processing stays in-house. If using an external API, data still leaves the premises, but you are not entrusting the provider with a customized model that contains your IP (as would be the case with fine-tuning). Moreover, running prompts on a local model keeps everything internal. In a comparison of approaches for a security-sensitive task, researchers noted that prompt-based use of a third-party model can raise data privacy concerns since prompts go to an external server, whereas fine-tuning typically involves downloading and using a model locally (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). However, if the model is local or the prompts do not reveal sensitive info, prompt engineering can avoid lengthy onboarding processes for new data – you use the model "frozen," which some legal/compliance teams find simpler to approve.
Human-aligned outputs through instruction: Prompt engineering allows users to inject human guidance on-the-fly. By crafting instructions ("Explain like I'm a patient", "Only cite from the given text"), data scientists can get outputs that align with the style or constraints needed, often more fine-grained control over output than a generic fine-tune would allow (When to use prompt engineering vs. fine-tuning - TechTarget). It's possible to enforce format (e.g. JSON output for an extraction task) just by prompt, which is extremely useful for integration into pipelines.

Limitations: Prompt engineering also comes with notable downsides and difficulties:

Unreliable accuracy and consistency: While a good prompt can greatly improve results, it is not as robust as having the model explicitly trained for the task. Prompted LLMs may still underperform a fine-tuned model on specialized tasks (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). In an experiment on phishing detection, using prompt engineering alone gave decent results, but the fine-tuned LLM reached an F1 score ~10 points higher and was much more reliable in various real-world conditions (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). This suggests that for critical applications (like diagnostic classification), prompt engineering might not attain the needed accuracy ceiling. Moreover, small changes in phrasing can yield different answers – getting stable output requires a lot of trial and error in prompt design. Consistency can be a problem: the model might follow the prompt well most of the time, but occasionally ignore an instruction, leading to an outlier error. This probabilistic behavior is hard to fully control without code or training.
Necessity of expertise and iteration: Crafting effective prompts is as much an art as science. It often requires iterative experimentation to find a prompt formulation that works best (Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons). Data scientists (or medical subject matter experts) must spend time testing and refining prompts, which can be non-trivial for complex tasks. There's a learning curve to understanding how the LLM responds. For example, to get a model to output a structured summary of a clinical study, one might have to try numerous prompt templates and example-based prompts. This process can be time-consuming and may feel like debugging – essentially tuning without gradient descent. In some cases, prompt engineering might involve learning prompt patterns like chain-of-thought or few-shot examples, which require expertise to apply correctly (Prompt Engineering vs. Fine-Tuning—Key Considerations and Best Practices - Nexla) (Prompt Engineering vs. Fine-Tuning—Key Considerations and Best Practices - Nexla).
Context length and data limitations: Prompts are limited by the model's context window (often a few thousand tokens). This means if you have very large documents (say a full clinical study report of dozens of pages), you cannot feed it all at once via prompt in current models. You might need to summarize or retrieve pieces (leading into techniques like RAG – Retrieval Augmented Generation). This limitation makes prompt-only solutions tricky for tasks like reviewing an entire submission dossier or mining a large health record: the prompt can only hold so much. Fine-tuning or specialized models might handle large data in chunks or via indexing, whereas a plain prompt has a hard cutoff.
Potential exposure of sensitive data: When using an external LLM API, your prompts (which may contain confidential text) are sent to the model provider. This raises privacy issues – for instance, a prompt with patient data or a new drug structure could inadvertently leak information to the model's training logs. Many healthcare firms have policies against putting PHI (Protected Health Information) into third-party prompts. There is also risk that the model might incorporate your prompt into its outputs for other users in some unpredictable way (open models like GPT-4 are not supposed to do this with immediate prompts, but the data is still seen by the service provider). Thus, prompt engineering with external models requires careful scrubbing of sensitive details or use of on-premise LLM solutions.
Difficulty in enforcing knowledge cut-off and truthfulness: An LLM will always rely on its internal knowledge (up to its training date) unless explicitly constrained. Prompt instructions like "Use the following document to answer" help, but the model might still blend in outside knowledge or make things up if not supervised. In fast-moving biomedical fields, prompt engineering alone may not ensure that the model only provides up-to-date, verified information. Without fine-tuning on new data or retrieval of latest info, a prompted model could be confidently outdated (e.g., not knowing about the latest approved therapies). This is why prompt-based systems often are paired with retrieval of recent data. Prompting also has limited ability to force the model to refrain from guessing; you can instruct "If unknown, say you don't know," but the model might not always obey.

Use Cases and Examples: Despite challenges, prompt engineering has proven very useful in many pharma/life-science settings, especially when time or data for fine-tuning are lacking. A concrete example is a recent Pfizer-sponsored challenge to automatically generate parts of clinical study reports. All participating teams tackled the problem by prompt engineering pre-trained LLMs (with carefully designed prompts and few-shot examples) rather than fine-tuning models from scratch (Using large language models for safety-related table summarization in… - Sheraz Khan) (Using large language models for safety-related table summarization in… - Sheraz Khan). This approach was successful in producing summaries of safety data tables, demonstrating that even in a highly regulated task like clinical documentation, cleverly prompting a model can yield valuable outputs. Prompt engineering is also frequently used for literature review and knowledge extraction: for instance, asking GPT-4 (with a prompt template) to read a provided journal article and highlight key outcomes or to compare two drug trial results. Researchers and clinicians have used prompt-based LLM queries to assist in hypothesis generation, by phrasing questions such as "Given these gene expression findings, what pathways might be involved in Disease X?" The LLM can provide insights pulling from its learned knowledge. Another domain is patient interaction: using prompt engineering to have an LLM rephrase complex medical information in layperson terms. By prompting the model with instructions to simplify language, pharma companies can create draft patient education materials quickly (Applications of LLMs in the Pharmaceutical Industry - by Juan Martinez - MantisNLP - Medium). In drug discovery, prompt-engineered models have even been experimented with for generating molecular hypotheses – for example, prompting a chemical-aware LLM to propose novel compounds given a target description. And beyond text, with multimodal capabilities emerging, one could prompt an LLM to analyze an image (like a histology slide description) if the model supports it.

Notably, prompt engineering is often combined with retrieval of domain data to compensate for LLM limitations. For example, a system might fetch relevant scientific papers and then prompt the model with, "Given the following data [insert excerpt], answer the question…". This Retrieval-Augmented Generation (RAG) approach avoids needing to fine-tune the model on the entire corporate knowledge base; instead the knowledge is provided at runtime (deepset Blog - Fine-tuning Large Language Models) (deepset Blog - Fine-tuning Large Language Models). Many pharma implementations use this hybrid of retrieval + prompt engineering to ensure up-to-date and factual answers (e.g. an LLM-powered chatbot for medical information that always includes snippets from approved documents in the prompt). Overall, prompt engineering shines for rapid prototyping and applications where using an existing powerful LLM off-the-shelf yields acceptable results. It emphasizes human-in-the-loop control of the model's behavior through smart querying, which can be very powerful when done well.

Comparative Analysis and Choosing the Right Approach

Each of these techniques – fine-tuning, distillation, and prompt engineering – has a distinct role in adapting LLMs for life science applications. The best choice depends on the specific needs, constraints, and goals of the project. Below is a summary comparison and considerations for choosing an approach in pharma/biotech contexts:

Customization vs. Convenience: Fine-tuning offers deep customization of an LLM's behavior (the model itself is changed to embed new knowledge), which tends to yield the highest accuracy on specialized tasks (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). Prompt engineering, by contrast, leaves the model unchanged and thus is far more convenient and immediate, though often with somewhat lower ceiling on performance (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models). Distillation sits in between: it creates a new model, but the goal is more about efficiency than improving accuracy beyond the teacher model. If a pharmaceutical AI solution demands maximal accuracy or adherence to specific output formats (for example, an FDA submission document generator), fine-tuning a model and rigorously evaluating it might be warranted. If the need is for a quick interactive tool to explore data or answer questions (where some errors are tolerable and can be caught by a human), prompt engineering with a strong base model might suffice initially.
Data availability: Fine-tuning requires task-specific or domain-specific data. In pharma, if you have a sizable proprietary dataset (e.g. thousands of annotated case reports, or a corpus of medicinal chemistry reactions), fine-tuning can strongly leverage it. Distillation also requires data, though it can be unlabeled – you generate it via the teacher. Prompt engineering requires no training data, only expertise in the domain to craft questions. Thus, prompt engineering is ideal when you lack a labeled dataset and want to utilize the model's pretrained knowledge immediately. Fine-tuning is ideal when you do have high-quality data and need the boost in performance it can provide. Knowledge distillation could be ideal when you have access to a teacher model's outputs (possibly even leveraging public data) and you need to create a lightweight model for deployment or to preserve some knowledge internally.
Computational resources and deployment: If the end deployment environment (a hospital server, a clinician's mobile app, etc.) has limited resources or needs offline operation, using a gigantic model may be impractical. Distillation becomes attractive here – you sacrifice a bit of performance to get a model that fits the environment (LLM distillation demystified: a complete guide - Snorkel AI) (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers). Fine-tuning does not reduce model size (it typically keeps the same parameter count (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers)), so a fine-tuned GPT-3 remains as large and costly to run as GPT-3 – something to keep in mind. Prompt engineering usually implies using the full-size model (often via cloud API), which can be expensive per query and may not meet latency requirements for real-time use. In such cases, one might fine-tune a moderate-sized model or distill a large model into a smaller one that can be deployed internally. For example, a pharma company might start with prompt engineering using GPT-4 for a prototype knowledge assistant; if the concept proves useful, they could then fine-tune an open-source 7B-parameter model on their data and perhaps distill it further, so the solution can run inexpensively on-premise.
Maintenance and adaptability: Prompt engineering is very adaptable: if requirements change, you just change the prompt. It's easy to update the behavior (just edit text) and try again. Fine-tuning a model is a heavier process – updating the model to new information means running a new fine-tuning job with updated data (or using techniques like continual learning). There's also the risk of a fine-tuned model becoming stale as knowledge progresses (deepset Blog - Fine-tuning Large Language Models). In fast-evolving biomedical fields, a strategy is to use prompt engineering combined with retrieval of current data (so the model always gets fresh context) rather than constantly re-tuning the model itself. Distillation, once done, is also static – if the teacher model was frozen in 2023, the distilled model won't know 2024 discoveries unless re-distilled or otherwise updated. Thus, for long-term maintainability in a dynamic domain, prompt engineering with retrieval might reduce the need to frequently retrain models for new data, whereas fine-tuned or distilled models may require scheduled re-training as new data arrives.
Regulatory and compliance factors: In highly regulated use-cases (like generating content for drug labels or patient communications), fine-tuning plus rigorous validation may be the only way to get a model that consistently meets the required standard (and even then, as noted, it's not guaranteed deterministic (Ostro - Why Fine Tuning is Not Fine for Pharma Marketing)). Prompt engineering alone might be too unpredictable in phrasing for such high-stakes output. On the other hand, fine-tuning might be disallowed if it means sending data to an external service, whereas prompt engineering could be done with manual entry of known-approved text segments. Compliance departments might prefer traceability that retrieval + prompt provides (since the source text can be cited, for example) over a black-box model's answer (Key Application Areas of LLMs in the Pharma and Life Science Sector - Comma Soft). This is one reason retrieval-augmented prompting is touted as a good solution for question-answering in pharma – the LLM is guided to output pieces of provided reference text, reducing the chance of unauthorized wording. Distillation doesn't inherently solve compliance issues except by making a model self-contained (you could embed some rules in the training data).

In practice, these approaches are not mutually exclusive. They can be combined to get the best of each. A common pattern in life sciences is: use prompt engineering and retrieval with a big LLM to quickly demonstrate a solution (for example, a chatbot that answers medical questions with references). If that gains traction, gather user feedback and examples to fine-tune a smaller in-house model for the task, improving accuracy and control. Then apply distillation to compress it for deployment in a resource-constrained setting (like a field tablet for clinical trial coordinators). By combining all three, one can go from idea to production: prompt engineering for ideation and prototyping, fine-tuning for precision, and distillation for efficiency.

Conclusion

Large Language Models hold great promise in pharma and life sciences, but realizing that promise requires choosing the right technique to adapt them to the task at hand. Fine-tuning an LLM endows it with specialized knowledge and top-tier performance on domain-specific tasks, which is invaluable for applications like expert decision support or research analysis – yet this comes with higher development overhead and maintenance demands. Distillation allows organizations to compress and deploy these capabilities at scale, enabling faster and cheaper models – an important step when integrating AI into clinical workflows or consumer health apps, although with some loss of fidelity from the original model. Prompt engineering, on the other hand, provides a nimble and powerful way to leverage LLMs immediately, trading some raw performance for ease of use, flexibility, and low startup cost. It has proven its worth in numerous pharma use cases, from generating study report summaries to assisting with patient inquiries, especially when combined with retrieval of up-to-date data.

For data scientists in pharma, the key is to assess the requirements: accuracy needs, data availability, inference budget, and regulations. If a model must output exactly correct regulatory language, fine-tuning (and extensive validation) is likely needed – perhaps supplemented by prompt-based instructions to fine-tune models or post-process outputs. If the goal is a broadly knowledgeable assistant to help researchers brainstorm, a carefully prompt-engineered query to GPT-4 or a similar model might deliver value on day one. If deploying AI at the bedside or in a resource-limited environment, a distilled model can make the difference between a feasible solution and none at all. In sum, fine-tuning, distillation, and prompt engineering are complementary tools. Mastering all three enables the creation of LLM-driven solutions in healthcare that are accurate, efficient, and practically deployable, accelerating innovation while respecting the unique challenges of this domain.

Sources: Fine-tuning definitions and benefits (deepset Blog - Fine-tuning Large Language Models) (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health) (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health); fine-tuning limitations (deepset Blog - Fine-tuning Large Language Models) (Ostro - Why Fine Tuning is Not Fine for Pharma Marketing) (Fine-Tuning Large Language Models for Specialized Use Cases - Mayo Clinic Proceedings: Digital Health). Distillation methodology and trade-offs (LLM distillation demystified: a complete guide - Snorkel AI) (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers) (LLM distillation demystified: a complete guide - Snorkel AI). Prompt engineering concepts (LLMs: Fine-tuning, distillation, and prompt engineering - Machine Learning - Google for Developers) (Key Application Areas of LLMs in the Pharma and Life Science Sector - Comma Soft) and use in pharma (Using large language models for safety-related table summarization in… - Sheraz Khan). Comparative insights on performance (Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models) and flexibility (Prompt Engineering vs. Fine-Tuning—Key Considerations and Best Practices - Nexla). Examples: Med-PaLM 2 medical QA performance (Google's Med-PaLM 2: A Paradigm Shift in Medical Education and Examination Preparation), Pfizer clinical report challenge (prompt-based) (Using large language models for safety-related table summarization in… - Sheraz Khan), Biomedical DistilBERT patent classifier (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC) (Needle in a haystack: Harnessing AI in drug patent searches and prediction - PMC), and others as cited throughout.