Instruction Tuning and Custom Instruction Libraries: Your Model’s Real ‘Operating Manual

Prompt Tricks Don’t Scale. Instruction Tuning Does. If you’ve ever shipped an LLM feature, you know the pattern: You craft a gorgeous prompt. It works… until real users show up. Suddenly your “polite customer support bot” becomes a poetic philosopher who forgets the refund policy. You craft a gorgeous prompt. It works… until real users show up. until Suddenly your “polite customer support bot” becomes a poetic philosopher who forgets the refund policy. That’s the moment you realise: prompting is configuration; Instruction Tuning is installation. prompting is configuration; Instruction Tuning is installation Instruction Tuning is how you teach a model to treat your requirements like default behaviour—not a suggestion it can “creatively interpret”. What Is Instruction Tuning, Really? Definition Instruction Tuning is a post-training technique that trains a language model on Instruction–Response pairs so it learns to: Instruction Tuning Instruction–Response pairs understand the intent behind a task (“summarise”, “classify”, “extract”, “fix code”) produce output that matches format, tone, and constraints reliably understand the intent behind a task (“summarise”, “classify”, “extract”, “fix code”) produce output that matches format, tone, and constraints reliably format, tone, and constraints In other words, you’re moving from: “Generate coherent text” “Generate coherent text” to: “Execute tasks like a dependable system component.” “Execute tasks like a dependable system component.” A quick intuition A base model may respond to: “Summarise this document” “Summarise this document” with something long, vague, and slightly dramatic. A tuned model learns: what counts as a summary what “key points” actually means how to keep it short, structured, and consistent what counts as a summary what counts as a summary what “key points” actually means how to keep it short, structured, and consistent Instruction Tuning vs Prompt Tuning: The Difference That Matters Dimension Instruction Tuning Traditional Prompt Tuning Where it acts Model weights (behaviour changes) Input text only Data need Needs many labelled examples Needs few examples Best for Long-term, repeated tasks (support, compliance, extraction) Ad-hoc tasks (one-off writing, translation) Persistence Behaviour sticks after training You redesign prompts repeatedly Barrier Higher (data + training setup) Lower (just write prompts) Dimension Instruction Tuning Traditional Prompt Tuning Where it acts Model weights (behaviour changes) Input text only Data need Needs many labelled examples Needs few examples Best for Long-term, repeated tasks (support, compliance, extraction) Ad-hoc tasks (one-off writing, translation) Persistence Behaviour sticks after training You redesign prompts repeatedly Barrier Higher (data + training setup) Lower (just write prompts) Dimension Instruction Tuning Traditional Prompt Tuning Dimension Dimension Instruction Tuning Instruction Tuning Traditional Prompt Tuning Traditional Prompt Tuning Where it acts Model weights (behaviour changes) Input text only Where it acts Where it acts Model weights (behaviour changes) Model weights (behaviour changes) Model weights Input text only Input text only Input text Data need Needs many labelled examples Needs few examples Data need Data need Needs many labelled examples Needs many labelled examples many Needs few examples Needs few examples Best for Long-term, repeated tasks (support, compliance, extraction) Ad-hoc tasks (one-off writing, translation) Best for Best for Long-term, repeated tasks (support, compliance, extraction) Long-term, repeated tasks (support, compliance, extraction) Long-term, repeated tasks Ad-hoc tasks (one-off writing, translation) Ad-hoc tasks (one-off writing, translation) Ad-hoc tasks Persistence Behaviour sticks after training You redesign prompts repeatedly Persistence Persistence Behaviour sticks after training Behaviour sticks after training You redesign prompts repeatedly You redesign prompts repeatedly Barrier Higher (data + training setup) Lower (just write prompts) Barrier Barrier Higher (data + training setup) Higher (data + training setup) Lower (just write prompts) Lower (just write prompts) If you’re running the same workflows every day, Instruction Tuning pays off fast. Instruction Tuning pays off fast The Custom Instruction Library: Your “Ammo Depot” Instruction Tuning is the strategy. A custom instruction library is the ammunition. custom instruction library It’s a curated dataset that encodes: what tasks you care about how they should be handled how outputs must look and sound what tasks you care about how they should be handled how outputs must look and sound Think of it as your model’s operating manual, written in training data. operating manual What a High-Quality Instruction Pair Must Contain Every example should have four parts. No shortcuts. Task type e.g., summarisation, translation, extraction, code repair, sentiment analysis Instruction Clear, unambiguous, with output constraints (format, length, tone) Input The raw material: document, conversation log, code snippet, medical notes, etc. Reference response The ideal output: correct, complete, consistent, and formatted Task type e.g., summarisation, translation, extraction, code repair, sentiment analysis Task type Instruction Clear, unambiguous, with output constraints (format, length, tone) Instruction Input The raw material: document, conversation log, code snippet, medical notes, etc. Input Reference response The ideal output: correct, complete, consistent, and formatted Reference response ideal A simple schema (JSONL) {"task":"refund_support","instruction":"Reply in friendly UK English. Confirm refund status and estimate payout time. Avoid jargon.","input":"Order #A91822. I requested a refund yesterday due to a faulty item. What's happening and when will I get the money?","output":"Hi! I can see refund request for order A91822 is approved and currently processing. You should receive the funds within 1–3 working days (depending on your bank). You don’t need to do anything else — we’ll notify you once it’s completed."} {"task":"refund_support","instruction":"Reply in friendly UK English. Confirm refund status and estimate payout time. Avoid jargon.","input":"Order #A91822. I requested a refund yesterday due to a faulty item. What's happening and when will I get the money?","output":"Hi! I can see refund request for order A91822 is approved and currently processing. You should receive the funds within 1–3 working days (depending on your bank). You don’t need to do anything else — we’ll notify you once it’s completed."} Notice what’s happening: tone, region, and format are all part of the spec. tone, region, and format are all part of the spec Design Principles That Actually Move the Needle 1) Coverage: hit the long tail, not just the happy path If you’re tuning for e-commerce support, don’t only include: “Where’s my parcel?” “I want a refund.” “Where’s my parcel?” “I want a refund.” Also include the messy real world: partial refunds missing items “Royal Mail says delivered but it isn’t” chargebacks angry customers who won’t provide an order number partial refunds missing items “Royal Mail says delivered but it isn’t” chargebacks angry customers who won’t provide an order number A model trained on only “clean” scenarios will panic the first time the input isn’t. 2) Precision: remove ambiguity from your instructions Bad instruction: “Handle this user request.” “Handle this user request.” Better instruction: “Classify the sentiment as Positive/Neutral/Negative, then give a one-sentence reason.” “Classify the sentiment as Positive/Neutral/Negative, then give a one-sentence reason.” Best instruction: “Return JSON exactly: {"label": "Positive|Neutral|Negative", "reason": "..."}. No extra text.” “Return JSON exactly: {"label": "Positive|Neutral|Negative", "reason": "..."}. No extra text.” 3) Diversity: vary inputs aggressively Include: short vs long inputs slang, typos, mixed languages formal tickets vs WhatsApp-style messages different difficulty levels short vs long inputs slang, typos, mixed languages formal tickets vs WhatsApp-style messages different difficulty levels Your production users are a chaos generator. Train accordingly. 4) Consistency: standardise output formats like you mean it If 200 “Order status” answers are formatted differently, the model will learn inconsistency. Pick a template and enforce it: fixed headings stable phrasing stable field order fixed headings stable phrasing stable field order Three Domain Examples (With Slightly More Realism) 1) Healthcare: extract case notes into a structured table (with safety) Task: clinical note extraction Instruction: extract fields, present as a table, avoid inventing missing values Task: Instruction: Input (example): Input (example): Patient: male, 45. Complains of cough and phlegm for 8 weeks, worsened with chest tightness for 1 week. History: hypertension x5 years, on nifedipine. CT: inflammation in both lower lobes. WBC: 12.5×10^9/L (high). Preliminary diagnosis: community-acquired pneumonia; hypertension grade 2. Patient: male, 45. Complains of cough and phlegm for 8 weeks, worsened with chest tightness for 1 week. History: hypertension x5 years, on nifedipine. CT: inflammation in both lower lobes. WBC: 12.5×10^9/L (high). Preliminary diagnosis: community-acquired pneumonia; hypertension grade 2. Output (example): Output (example): Category Details Demographics Age: 45; Sex: Male Presenting complaint Cough and phlegm for 8 weeks; worse with chest tightness for 1 week History Hypertension for 5 years; nifedipine Tests CT: bilateral lower-lobe inflammation; WBC: 12.5×10^9/L (elevated) Preliminary diagnosis Community-acquired pneumonia; Hypertension (grade 2) Category Details Demographics Age: 45; Sex: Male Presenting complaint Cough and phlegm for 8 weeks; worse with chest tightness for 1 week History Hypertension for 5 years; nifedipine Tests CT: bilateral lower-lobe inflammation; WBC: 12.5×10^9/L (elevated) Preliminary diagnosis Community-acquired pneumonia; Hypertension (grade 2) Category Details Category Category Details Details Demographics Age: 45; Sex: Male Demographics Demographics Age: 45; Sex: Male Age: 45; Sex: Male Presenting complaint Cough and phlegm for 8 weeks; worse with chest tightness for 1 week Presenting complaint Presenting complaint Cough and phlegm for 8 weeks; worse with chest tightness for 1 week Cough and phlegm for 8 weeks; worse with chest tightness for 1 week History Hypertension for 5 years; nifedipine History History Hypertension for 5 years; nifedipine Hypertension for 5 years; nifedipine Tests CT: bilateral lower-lobe inflammation; WBC: 12.5×10^9/L (elevated) Tests Tests CT: bilateral lower-lobe inflammation; WBC: 12.5×10^9/L (elevated) CT: bilateral lower-lobe inflammation; WBC: 12.5×10^9/L (elevated) Preliminary diagnosis Community-acquired pneumonia; Hypertension (grade 2) Preliminary diagnosis Preliminary diagnosis Community-acquired pneumonia; Hypertension (grade 2) Community-acquired pneumonia; Hypertension (grade 2) Note: In medical settings, you typically add safety rules (“don’t diagnose beyond provided evidence”) directly into the instruction library. Note: In medical settings, you typically add safety rules (“don’t diagnose beyond provided evidence”) directly into the instruction library. 2) Programming: fix broken Python (and explain why) Input (broken code): Input (broken code): def calculate_average(nums): total = 0 for n in nums total += n return total / len(nums) def calculate_average(nums): total = 0 for n in nums total += n return total / len(nums) Output (fixed + explanation): Output (fixed + explanation): def calculate_average(nums): total = 0 for n in nums: # missing colon fixed total += n return total / len(nums) def calculate_average(nums): total = 0 for n in nums: # missing colon fixed total += n return total / len(nums) Explanation: for loops in Python require a colon (:). Without it, the interpreter can’t parse the loop body. for : (Also: avoid naming a variable sum—it shadows Python’s built-in sum().) (Also: avoid naming a variable sum sum() 3) Education: generate a Year 4 maths word problem (UK flavour) Instruction: Generate one word problem Topic: two-digit × one-digit multiplication Include: scenario, known facts, question Use £ and UK context Generate one word problem one Topic: two-digit × one-digit multiplication Include: scenario, known facts, question Use £ and UK context Output: Scenario: School fair Known facts: Each ticket costs £24. A parent buys 3 tickets. Question: How much do they pay in total? Scenario: Known facts: £24 3 Question: Implementation Workflow: From Library to Tuned Model Instruction Tuning is a pipeline. If you skip steps, you pay later. Step 1: Build the dataset Sources Internal logs: support tickets, agent replies, summaries Public datasets: Alpaca, FLAN, ShareGPT (filter aggressively) Human labelling: for tasks where correctness matters Internal logs: support tickets, agent replies, summaries Internal logs: Public datasets: Alpaca, FLAN, ShareGPT (filter aggressively) Public datasets: Human labelling: for tasks where correctness matters Human labelling: Cleaning checklist remove vague instructions (“handle this”, “do the thing”) remove wrong/incomplete outputs deduplicate near-identical samples standardise formatting and field names remove vague instructions (“handle this”, “do the thing”) remove wrong/incomplete outputs deduplicate near-identical samples standardise formatting and field names Split Train: 70–80% Validation: 10–15% Test: 10–15% Train: 70–80% Validation: 10–15% Test: 10–15% No leakage. No overlap. Step 2: Choose the base model (with reality constraints) Pick based on: task complexity deployment constraints compute budget task complexity deployment constraints compute budget A practical rule: 7B–13B models + LoRA work well for many enterprise workflows bigger models help with harder reasoning and richer generation if you need on-device or edge, plan for quantisation and smaller footprints 7B–13B models + LoRA work well for many enterprise workflows 7B–13B bigger models help with harder reasoning and richer generation if you need on-device or edge, plan for quantisation and smaller footprints Step 3: Fine-tune strategy and hyperparameters Typical starting points (LoRA): learning rate: 1e-4 to 5e-5 batch size: 8–32 (use gradient accumulation if needed) epochs: 3–5 early stopping if validation stops improving learning rate: 1e-4 to 5e-5 1e-4 to 5e-5 batch size: 8–32 (use gradient accumulation if needed) 8–32 epochs: 3–5 3–5 early stopping if validation stops improving LoRA is popular because it’s efficient: you train small adapter matrices instead of all weights. Step 4: Evaluate like you’re going to ship it Quantitative (depends on task) classification accuracy (for extract/classify) BLEU/ROUGE (for summarise/translate — imperfect but useful) perplexity (language quality proxy) classification accuracy (for extract/classify) BLEU/ROUGE (for summarise/translate — imperfect but useful) perplexity (language quality proxy) Qualitative (the “would you trust this?” test) Get 3–5 reviewers to score: instruction adherence completeness format correctness domain alignment instruction adherence completeness format correctness domain alignment Also run scenario tests: 10–20 realistic edge cases. scenario tests Step 5: Deploy + keep tuning After deployment: log response times, failures, “format drift” collect bad outputs and user feedback convert them into new instruction pairs periodically re-tune log response times, failures, “format drift” collect bad outputs and user feedback convert them into new instruction pairs periodically re-tune Instruction libraries are living assets. A Practical Case Study: E‑Commerce Support Goal Teach a model to handle: order status refunds product recommendations order status refunds product recommendations with: consistent format friendly UK English accurate, policy-compliant answers consistent format friendly UK English accurate, policy-compliant answers Dataset (example proportions) 300 order status 400 refunds 300 recommendations 300 order status 400 refunds 300 recommendations Training setup (example) base model: a chat-tuned 7B model LoRA rank: 8 LoRA dropout: 0.05 epochs: 3 one consumer GPU class machine (or cloud instance) base model: a chat-tuned 7B model LoRA rank: 8 LoRA dropout: 0.05 epochs: 3 one consumer GPU class machine (or cloud instance) Deployment trick Quantise to 4-bit / 8-bit for serving efficiency, then integrate with your order/refund systems: model drafts the response the system injects verified order facts the final message is generated with hard constraints model drafts the response the system injects verified order facts the final message is generated with hard constraints This hybrid approach reduces hallucinations dramatically. Common Failure Modes (And Fixes) 1) “We don’t have enough data” If you’re below ~500 high-quality pairs, results can be shaky. Fix: generate candidate pairs with a strong model have humans review and correct do light data augmentation (rephrase, swap names, vary order numbers) generate candidate pairs with a strong model have humans review and correct do light data augmentation (rephrase, swap names, vary order numbers) 2) Overfitting: great on train, bad on test Fix: reduce epochs add diversity add dropout/weight decay use early stopping based on validation performance reduce epochs add diversity add dropout/weight decay use early stopping based on validation performance 3) Domain terms confuse the model Fix: add “term explanation” examples include lightweight domain Q&A pairs so the model builds vocabulary add “term explanation” examples include lightweight domain Q&A pairs so the model builds vocabulary 4) Output format keeps drifting Fix: standardise reference outputs make formatting requirements explicit add negative examples (“do not output extra text”) standardise reference outputs make formatting requirements explicit add negative examples (“do not output extra text”) The Future: Auto-Instructions, Multimodal, and Edge Fine-Tuning Where this is heading: Auto-instruction generation: models produce draft datasets, humans curate multimodal tuning: text + images + audio instructions become normal (e.g., “analyse this product photo and write a listing”) lighter tuning on edge devices: smaller models + efficient adapters + on-device updates Auto-instruction generation: models produce draft datasets, humans curate Auto-instruction generation multimodal tuning: text + images + audio instructions become normal (e.g., “analyse this product photo and write a listing”) multimodal tuning lighter tuning on edge devices: smaller models + efficient adapters + on-device updates lighter tuning on edge devices Final Take Prompting is a great way to ask a model to behave. Instruction Tuning is how you teach it to behave. ask teach If you want reliable outputs across many tasks, stop writing prompts like spells—and start building a custom instruction library like a real product asset: custom instruction library comprehensive coverage precise instructions diverse inputs consistent outputs comprehensive coverage precise instructions diverse inputs consistent outputs That’s how you get “one fine-tune, many tasks” without babysitting the model forever.