Workshop Companion · March 2026

Top 5 Uses of Prompt Engineering in Life Sciences

A practical guide for lab scientists using Claude, ChatGPT and other LLMs to accelerate research workflows — from literature review to regulatory documentation.

Core Techniques

Literature Search & Synthesis

Query, filter and summarise thousands of papers in minutes. Extract methods, results and contradictions across corpora.

Experimental Data Interpretation

Analyse assay results, identify outliers, suggest follow-ups. Translate raw instrument output into actionable insight.

Protocol & SOP Drafting

Generate first drafts of lab protocols, regulatory documents and study reports. Enforce template compliance and terminology.

Target & Molecule Analysis

Interrogate compound properties, predict ADMET profiles, compare target biology. Accelerate early-stage discovery decisions.

Lab Notebook & Report Writing

Structure ELN entries, draft experiment summaries and translate findings into publication-ready language.

Advanced Techniques

Managing Token Limits & Context Windows

Why your 50-page report won't fit in a single prompt — chunking strategies, compression, and multi-turn decay.

Built-In Search & Tools

Web search, code execution, image analysis, PDF extraction, file creation — going beyond basic chat.

MCP Servers & External Connectors

Connecting to live scientific databases — ChEMBL, ClinicalTrials.gov, Open Targets, bioRxiv and more.

A word of caution: Every technique on this page should be paired with human review. LLMs are powerful reasoning assistants, not infallible oracles. They hallucinate references, make confident-sounding errors, and lack access to your proprietary data unless you provide it. Prompt engineering helps you get better outputs — but the scientist remains the final authority.

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Literature Search & Synthesis

How prompt engineering transforms scientific literature review

Structured Query Prompts

Define your search scope precisely: disease, target class, assay type, date range. Ask the LLM to return structured tables of papers with authors, key findings and relevance scores — not just prose summaries."Find 10 papers on KRAS G12C inhibitors published since 2022. Return a table: authors, journal, key finding, relevance to resistance mechanisms."

Contradiction & Gap Detection

Prompt the model to compare conclusions across papers. "Which studies disagree on mechanism X?" and "What open questions remain after these 10 papers?" surface blind spots that human reviewers miss under time pressure.

Iterative Refinement

Start broad, then narrow. First prompt: high-level landscape. Follow-up: drill into specific sub-topics. Chain prompts to move from 500 abstracts → 30 relevant papers → 5 key findings. Each step narrows the funnel.

Citation Verification

Always prompt the model to provide verifiable details (DOI, journal, year). LLMs hallucinate references — instruct it to flag uncertainty and distinguish confirmed from inferred claims."For each claim, mark [VERIFIED] if you can provide a DOI, or [UNVERIFIED] if you are inferring from training data."

Experimental Data Interpretation

How prompt engineering accelerates analysis of lab results

Context-Rich Data Prompts

Paste raw data (CSV, tables) and specify the assay type, expected ranges, and controls. The more experimental context you provide, the more targeted the analysis."Here is IC50 data for 12 compounds against EGFR. Identify outliers, rank potency, and flag any dose-response curves that look unreliable."

Statistical Reasoning Prompts

Ask for specific statistical assessments: "Are these differences significant at p<0.05?" "What is the coefficient of variation across replicates?" Prompt the model to show its working and state assumptions explicitly.

Follow-Up Experiment Design

After interpreting results, prompt: "Based on these findings, what are the three most informative follow-up experiments and why?" Forces the model to reason about what the data does and does not tell you.

Sanity-Check Prompting

Ask the model to critique its own interpretation: "What alternative explanations exist for this result?" and "What would make you change your conclusion?" Reduces confirmation bias in AI-assisted analysis.

Protocol & SOP Drafting

How prompt engineering produces compliant, usable lab documents

Template-Driven Prompts

Provide the SOP template structure (purpose, scope, materials, procedure, safety) in the prompt. The model fills each section while respecting your company's format, terminology and numbering conventions.

Specificity Over Generality

"Write a protocol for HPLC analysis of mAb charge variants using a cation exchange column" beats "Write an HPLC protocol." Include instrument model, buffer recipes, column specs — the more context, the more usable the output.

Persona & Audience Prompts

"Write this for a junior lab technician with 6 months of experience" vs "Write this for a PhD-level scientist." Adjusting audience in the prompt controls the level of detail, safety warnings and assumed knowledge."Audience: new hire, BSc Chemistry, first week in the cell culture lab. Include all safety steps."

Regulatory Cross-Check

Prompt the model to flag where the draft may conflict with GLP/GMP requirements. "Review this protocol and highlight any steps that would not meet ICH Q2 validation guidelines." Use as a first-pass compliance check, not a final authority.

Target & Molecule Analysis

How prompt engineering supports early-stage discovery reasoning

Structured Property Queries

"Compare these 5 compounds on MW, LogP, HBD, HBA, PSA and Lipinski compliance. Return a table." Attach SMILES strings or names. The model organises known data and flags missing values rather than guessing.

Target Biology Interrogation

"Summarise the known biology of [target], its role in [disease], selectivity concerns with related family members, and the current clinical landscape." Chain follow-ups to drill into binding sites or resistance mechanisms.

ADMET Risk Assessment Prompts

"Based on this compound's structure, what ADMET liabilities would you predict and why?" Ask for reasoning, not just predictions. Prompt the model to cite structural alerts (e.g. anilines, Michael acceptors) explicitly.

Honest Limitations Prompting

LLMs do not run docking or simulations — they reason over training data. Always prompt: "What confidence do you have in this assessment and what would you need to verify experimentally?" Avoids over-reliance on AI opinions."Rate your confidence 1–5 for each claim and explain what experimental evidence would change your answer."

Lab Notebook & Report Writing

How prompt engineering improves documentation quality and speed

Structured Entry Prompts

"Convert these bullet-point bench notes into a formal ELN entry with: objective, materials, method, observations, results and conclusions." Transforms rough notes into auditable records in seconds.

Tone & Register Control

"Rewrite this in formal scientific register suitable for a regulatory submission" vs "Summarise this for a non-technical project sponsor." Same data, different outputs — the prompt controls voice, detail level and jargon.

Batch Summarisation

Process multiple experiments at once: "Here are results from 8 plate reads. For each, provide a 3-sentence summary covering: what was tested, key result, and next step." Scales documentation without scaling effort.

Self-Review Prompting

After drafting, prompt: "Review this report for: missing context, unsupported claims, inconsistent terminology, and unclear methodology." The model acts as a first-pass editor before human review.

Managing Token Limits & Context Windows

Why your 50-page assay report won't fit in a single prompt — and what to do about it

Understanding Context Windows

Every LLM has a finite context window — total text (measured in "tokens") it can hold. Claude Opus/Sonnet supports ~200K tokens; GPT-4o ~128K. One token ≈ 0.75 words. A 20-page PDF is ~8K–12K tokens. Sounds generous, but a full IND submission, multi-study report, or 50 papers will exceed it. When you hit the limit, the model silently drops older content — with no warning about what was lost.Rule of thumb: 1 page of dense scientific text ≈ 500–700 tokens. A 100-page regulatory filing ≈ 50K–70K tokens — roughly half of GPT-4o's entire window.

Chunking Strategy for Long Documents

Don't paste an entire CMC dossier into one prompt. Chunk by study, assay, or heading. Process each chunk separately into a structured summary. Then feed all summaries together for cross-document synthesis. This "map-reduce" pattern avoids overflow and produces higher-quality results because the model gives full attention to each section."I will give you 6 stability study reports one at a time. For each, return: product, conditions, time points tested, key findings, any out-of-spec results. I'll then ask you to compare them."

Prompt Compression Techniques

Remove boilerplate before sending. Strip headers, footers, page numbers, TOC entries, and repeated disclaimers. Send CSV rather than formatted tables — far fewer tokens. For papers, send abstract + results + discussion and omit methods you already understand. Every token saved is a token the model can use for reasoning.Practical test: paste your content and ask "How many tokens is this?" before building your real prompt around it.

Conversation Memory & Multi-Turn Decay

In a long conversation, effective attention to early messages degrades — even within the context window. Critical instructions from message 1 may be partially "forgotten" by turn 20. Re-state key constraints (species, target, assay type, output format) periodically. If accuracy matters, start fresh rather than continuing a 30-turn thread. Re-attach your reference document rather than assuming the model "remembers" it.

File Uploads vs Copy-Paste

Both Claude and ChatGPT support direct file upload (PDF, CSV, XLSX, images). Uploading is almost always better: the model handles structure, and you avoid token-heavy formatting artefacts. For analytical data (HPLC, plate reader), upload the raw CSV/Excel and prompt the model to analyse programmatically using its code execution tool — more reliable than asking it to eyeball pasted numbers."I've uploaded an Excel file with 96-well plate reader data (absorbance at 450nm). Calculate mean, SD and %CV for each condition. Flag any wells with CV > 15%."

When to Use "Deep Research" Mode

Both Claude and ChatGPT offer research modes for extended multi-step searches. Use for landscape questions needing dozens of sources: "What are the current clinical-stage KRAS G12C inhibitors and how do their selectivity profiles compare?" These modes manage their own token budget internally. However, they are slow (minutes, not seconds) and not suited to iterative back-and-forth. Use for the opening scan, then switch to normal chat for drilling down.

Built-In Search & Tools

Going beyond chat — web search, code execution, file analysis and vision capabilities

Web Search for Current Literature

Claude and ChatGPT can search the web during conversation. Critical because training data has a cutoff — the model may not know about a Phase III readout from last month or a recently retracted paper. Explicitly trigger search by asking for current information. The model cites sources with URLs you can verify. Always verify — search results are not peer-reviewed."Search for recent publications on GLP-1 receptor agonists in NASH/MASH. Focus on results published after January 2025. Provide DOIs where available."

Code Execution for Data Analysis

Both platforms execute Python in a sandboxed environment — transforming the LLM from reasoning tool to analytical one. Upload CSV/Excel and ask for real statistical tests, plots, or data cleaning. For dose-response curves, ask for a 4PL fit. For plate data, Z-prime calculations. The code runs on real data, not hallucinated approximations. Ask to see the code to verify methodology."Upload: plate_data.xlsx. Fit a 4-parameter logistic curve to each compound. Calculate IC50, Hill slope, R². Generate a publication-quality plot. Show me the Python code."

Image & Document Vision

Modern LLMs interpret images: gels, Western blots, TLC plates, HPLC chromatograms, microscopy, handwritten notebook pages. Upload and ask for interpretation. Useful for quick reads ("How many bands at approximately what MWs?") but the model cannot quantify band intensity precisely or replace densitometry software. Best for triage and preliminary reads, not regulatory data.

PDF Analysis & Extraction

Upload a paper, patent, or protocol PDF and ask the model to extract specific information. Far more efficient than reading a 40-page patent manually. The model reads the full document and cross-references between sections — ask it to check for internal contradictions."From this ICH guideline, list every requirement that applies to stability testing of monoclonal antibodies."

Artefact & File Creation

Claude generates downloadable files — Word, Excel, PowerPoint, HTML, code — directly in conversation. Go from analysis to deliverable in a single workflow: "Analyse this data, then create a formatted Word report with tables, figures and conclusions suitable for a study report appendix." Eliminates the copy-paste-into-Word bottleneck.

Structured Output & Formatting

For extraction tasks, instruct the model to return JSON, CSV, or Markdown tables. Critical when feeding output into downstream tools (LIMS, ELN, GraphPad, R). Without format instructions, the model defaults to prose — readable but not pipeable."Return results as JSON: [{gene, function, disease_association, evidence_strength, key_reference}]"

MCP Servers & External Connectors

Connecting LLMs to live scientific databases — ChEMBL, ClinicalTrials.gov, Open Targets, bioRxiv and more

What Is MCP (Model Context Protocol)?

MCP is an open protocol that lets LLMs call external tools and databases in real time during a conversation. Instead of relying on static training data, MCP connectors query live APIs — searching ChEMBL for compound bioactivity, pulling current trial status from ClinicalTrials.gov, or fetching the latest preprints from bioRxiv. Claude supports MCP natively. The model decides when to call the tool, retrieves structured data, and reasons over fresh results rather than memorised facts.

ChEMBL Connector

Search compounds by name, SMILES, or ChEMBL ID. Retrieve bioactivity data (IC50, Ki, EC50) for specific compound–target pairs. Look up mechanism of action for approved drugs. Pull ADMET-related properties for drug-likeness. Returns structured data the model can immediately analyse and compare."Search ChEMBL for all compounds with reported IC50 against BACE1. Return top 10 most potent with ChEMBL IDs, IC50 values, and molecular weight."

ClinicalTrials.gov Connector

Search trials by indication, sponsor, drug, or phase. Get detailed protocol info: eligibility criteria, endpoints, site locations. Analyse outcome measures across trials. Find principal investigators in a therapeutic area. Powerful for competitive intelligence: "Find all Phase II/III trials in NASH that started after January 2025 and compare their primary endpoints."

Open Targets Connector

Query for target–disease associations, genetic evidence, drug tractability, and pathway information. Particularly useful for target validation: "What is the genetic evidence linking LRRK2 to Parkinson's disease?" Returns structured GraphQL data aggregating GWAS, functional genomics, and known drugs — far richer than asking the model to recall from training.

bioRxiv Connector

Search preprints by keyword, author, or category. Check whether a preprint has been published in a peer-reviewed journal. Useful in fast-moving fields (cryo-EM, single-cell, AI/ML in drug discovery) where preprints precede journal publication by 6–12 months."Search bioRxiv for preprints on AlphaFold3 in drug discovery from the last 6 months. For each, tell me if it's been published in a journal yet."

Chaining Connectors in One Workflow

The real power emerges when you chain connectors. (1) Open Targets → top genetically-validated targets for a disease. (2) ChEMBL → existing chemical matter against the top target. (3) ClinicalTrials.gov → what's already in clinical development. (4) bioRxiv → recent novel approaches. A target-to-landscape assessment in one conversation that would normally take days."Open Targets: top 5 targets for ALS. For the top target, search ChEMBL for sub-micromolar compounds, then check ClinicalTrials.gov for active trials."

Google Drive, Gmail & Calendar

Beyond scientific databases, Claude connects to workplace tools. Search Drive for a study report without leaving the conversation. Pull emails about a project. Check calendar for review meetings. "Find the latest formulation development report in my Drive and summarise the key stability findings."

Limitations & Honest Caveats

MCP connectors query live databases, but the model's interpretation still depends on training. It may misinterpret a ChEMBL assay type or conflate similar trials. Connectors are subject to API rate limits and may not return exhaustive results for broad queries. The model does not access proprietary databases (SciFinder, Reaxys, internal LIMS) unless specifically configured. Always treat MCP-assisted research as a starting point for expert review.