Designing the Brain of Your AI Product
How to Use Prompts, RAG, Fine-Tuning, and Transfer Learning to Make LLMs Actually Useful
Most AI products don’t fail because the model is “not powerful enough.”
They fail because the context you give the model is wrong or weak.
Wrong or vague instructions → garbage outputs
Missing or outdated data → hallucinations
Overloaded prompts → confused responses
Premature fine-tuning → cost with no real gain
This is the territory of context engineering: everything you do to control what the model sees and how it thinks before it produces an answer.
You can think of it as the AI equivalent of product requirements: if you feed chaos in, you get chaos out.
This article goes deep into:
Why context engineering matters
Prompt engineering: how to give good instructions
Retrieval-Augmented Generation (RAG): how to feed the right data
Fine-tuning: when you really need to change the model itself
Transfer learning & distillation: making small models smarter
A decision framework: prompt vs RAG vs fine-tuning vs transfer learning
A practical checklist for AI product managers
Why context engineering matters?
Modern large language models (LLMs) are trained on vast text corpora and learn to predict the next token in a sequence. They’re extremely good at:
Understanding natural language
Following instructions (if they’re clear)
Combining knowledge from many domains
But they have two fundamental limits:
They only know what was in their training data (plus any updatable memory).
They only see what fits inside the context window for each request.
To work around those constraints, you need to control three things:
Instructions – what exactly you’re asking it to do (prompt engineering)
External data – what information it should base its answer on (RAG)
Internal parameters – what knowledge and behaviour are “baked into” the model (fine-tuning, transfer learning)
Doing this well is what turns “a clever chatbot” into a reliable product.
Prompt Engineering
Prompt engineering is the art of telling the model:
Who it should act as
What it should do
How it should format the answer
What constraints it must obey
Modern guides describe prompt engineering as a key way to customise model behaviour without retraining, using patterns like zero-shot, few-shot, and chain-of-thought prompting.
1. Core principles
You don’t need 1,000 “magic prompts.” You need a few principles:
Be explicit about the role
“Act as a senior product manager for B2B SaaS…”
“Act as an expert meeting note-taker…”
Roles help the model adopt the right style and level of detail.
Specify the task clearly
“Summarise this meeting transcript in 150–200 words.”
“Extract all action items with owners and due dates.”
“Propose 3 product ideas and evaluate them on impact vs effort.”
Vague requests (“Help me with this”) = vague answers.
Define the output format
JSON schema for APIs
Tables for dashboards
Bullet lists for summaries
Headings and sections for reports
This makes integration and evaluation much easier.
Constrain behaviour
“If information is missing, say ‘I don’t know’ rather than guessing.”
“Do not fabricate URLs or email addresses.”
“Only use facts present in the provided context.”
Constraints are how you reduce hallucinations.
Encourage step-by-step thinking
Techniques like chain-of-thought (CoT) ask the model to reason in steps. Research shows CoT prompting improves performance on multi-step reasoning tasks by explicitly asking the model to explain its thinking before giving the final answer.“First list out the steps you would take, then provide the final answer.”
“Think step-by-step. Don’t skip any steps.”
Use examples (few-shot prompting)
Few-shot prompting gives a couple of examples to demonstrate the pattern you want. Studies and practitioner guides show that providing 2–5 well-chosen examples can significantly improve the model’s consistency for structured tasks.Show one or two “good” Q&A pairs
Show an example of the exact format you expect
Iterate like an engineer
Treat prompts as code:Observe failure modes (hallucination, verbosity, missing fields)
Add constraints or clarifications
Refactor into reusable templates
2. A reusable prompt skeleton
For many product tasks, you can use a basic structure like:
Role: “You are a [role]…”
Task: “Your job is to [task].”
Input: “Here is the input:…”
Rules: “Follow these rules: 1) … 2) … 3) …”
Output format: “Respond in this exact JSON/table/markdown format: …”
Reasoning: “Think step-by-step, then provide the final answer only in the specified format.”
Once you have a handful of such templates, you can plug them into your product for:
Meeting summaries
Ticket triage
Requirements generation
User interview synthesis
Experiment analysis
Prompt engineering is always the first lever you should pull before touching data pipelines or model weights.
RAG:
Prompting only uses what’s already in the model’s head. But most products need to answer questions based on your own data:
Internal docs and wikis
Notion / Confluence spaces
CRM or ticketing data
Legal and policy documents
Domain-specific knowledge bases
You could stuff everything into the prompt, but:
Context windows are limited
Quality degrades when context gets very long
You don’t want to send sensitive or irrelevant data every time
This is where Retrieval-Augmented Generation (RAG) comes in.
1. What is RAG?
RAG connects an LLM to an external knowledge base. When a user asks a question:
You retrieve the most relevant pieces of information
You augment the prompt with those pieces
You ask the model to generate an answer that uses that context
Cloud, infrastructure, and AI vendors describe RAG as a pattern that grounds LLM outputs in authoritative, up-to-date data, reducing hallucinations and letting the system answer from your own docs or databases.
2. How a RAG pipeline really works
A typical pipeline has two phases:
A. Building the knowledge base
Ingest sources
PDFs, docs, HTML, transcripts, spreadsheets, database rows, etc.
Chunk the content
Split long documents into smaller, meaningful chunks (e.g., paragraphs, sections, code blocks)
Choose chunk size and overlap based on the domain (code vs marketing copy vs legal docs)
Embed the chunks
Convert each chunk into a vector embedding using an embedding model
Embeddings map semantically similar text to nearby points in a vector space
Store in a vector database
Save each embedding + original text + metadata (doc ID, section, timestamp, permissions)
Use a vector store that supports similarity search, filtering, and access control
Guides from vector DB providers and cloud platforms suggest that chunking strategy, embedding model choice, and metadata design significantly affect retrieval quality.
B. Answering a query
User asks a question
“What are the key changes in the latest privacy policy?”
“What are my top three priorities for tomorrow, based on my tasks and meetings?”
Embed the query
Convert the user’s query into a vector using the same embedding model
Retrieve similar chunks
Search the vector database for the most similar embeddings (top-k retrieval)
Optionally filter by metadata (e.g., only that user’s docs, or only last 3 months)
Construct the augmented prompt
System instructions +
Retrieved chunks as “context” +
The user’s query
Example structure:
“Answer the question using only the context below. If the answer isn’t present, say you don’t know.
Context:
[chunk 1]
[chunk 2]
[chunk 3]Question:
[user’s question]”
Call the LLM
The model reads the context + query and generates an answer grounded in that context
This lets you keep your knowledge base outside the model, update it frequently, and still give the model just enough context to be smart.
3. Common mistakes and best practices
Real-world teams report that most RAG implementations fail to reach production because of poor retrieval design, not model quality. (kapa.ai)
Key pitfalls:
Bad chunking
Chunks that are too small lose context
Chunks that are too big waste tokens and dilute relevance
Fix: tune chunk size per use case and use overlap
No metadata or filtering
Searching across the entire corpus for every query is slow and often irrelevant
Fix: tag chunks with doc type, owner, date, product area, etc. Use filters.
No retrieval evaluation
Teams look only at final answers, not whether retrieved documents were relevant
Fix: evaluate retrieval independently (precision / recall / relevance scores)
No guardrails on context usage
If you don’t tell the model to only use the provided context, it may “mix in” its own prior knowledge
Fix: explicit instructions (“Answer using only the context. If missing, say you don’t know.”)
Ignoring user experience
RAG isn’t just backend; UX matters:
Show citations or links to source documents
Let users inspect the retrieved context
Make it easy to correct wrong answers
4. Hierarchical and advanced RAG patterns
Some tasks, like summarising multiple long documents, break naive RAG:
The query “Summarise this entire folder” doesn’t match any specific chunk
You don’t want random patches of “summary” text; you want a coherent overview
Hence hierarchical RAG patterns:
First summarise each document individually
Then summarise the collection using these per-document summaries as context
Optionally “zoom in” into sections based on follow-up questions
Cloud vendors and practitioners recommend such multi-step, hierarchical strategies for large corpora to keep context size manageable while preserving high-level meaning. (Microsoft Learn)
As an AI PM, you don’t need to implement all of this yourself, but you must understand when simple “top-k chunk retrieval” is not enough and when you need multi-step pipelines.