AI Integrationadvanced

Building RAG Applications

Retrieval-augmented generation looks simple in a demo and stays simple until your knowledge base is bigger than a thousand documents, chunks overlap badly, or relevance scores stop making sense. This is my end-to-end RAG playbook: document processing, embedding pipelines, retrieval tuning, prompt design, and the evaluation harness that tells you whether changes are actually improving results.

120 min7 steps

Steps

Tools

Outcomes

advanced

Difficulty

Technologies used

OpenAIPineconeLangChainNext.js

The methodology

The phases, in order

Each phase below is something I actually run in a project. The descriptions are how I think about the work, not abstract definitions.

Phase

Phase 1 of 7

Vector Database Setup

I configure Pinecone or pgvector depending on scale. For under a million chunks pgvector inside the existing Postgres is simpler and cheaper. Beyond that, a dedicated vector store is worth the operational cost. Index dimension matches the embedding model exactly, set at creation time.

Phase

Phase 2 of 7

Document Ingestion and Chunking

Documents get normalized to clean text, stripped of boilerplate, then chunked by semantic boundary, not by fixed character count. I aim for chunks of 400 to 800 tokens with a small overlap. Each chunk carries source metadata: document id, page, section heading. This metadata is what makes citations possible later.

Phase

Phase 3 of 7

Embedding Pipeline

The embedding job is idempotent and batched, with content hashes so re-ingesting unchanged documents is free. I track which embedding model was used per row so I can swap models without losing track of which chunks need re-embedding. See the AI application blueprint for the wider system shape.

Phase

Phase 4 of 7

Hybrid Retrieval

Pure vector search misses exact matches, pure keyword search misses synonyms. I combine both with reciprocal rank fusion, then re-rank the top results with a cross-encoder for the highest-quality hits. This single step is the difference between a chatbot that hallucinates and one that grounds answers.

Phase

Phase 5 of 7

Prompt Engineering with Citations

The prompt template includes a clear instruction to answer only from the retrieved context and to cite chunk ids. I keep prompts in version control and write a test for each common failure mode. When the model has no good context, the instruction is to say so rather than guess. That single rule removes most embarrassing answers.

Phase

Phase 6 of 7

Generation and Streaming

Responses stream to the client with inline citation markers. The UI renders citations as hoverable tooltips with the original source. For long answers I limit context to the top-N highest-scoring chunks and add a hard token budget. Without budgets, costs balloon on edge cases.

Phase

Phase 7 of 7

Evaluation and Continuous Improvement

I build a small eval set of representative questions with known good answers, then run it on every prompt or model change. Metrics include faithfulness, citation accuracy, and answer relevance. Without evals you cannot improve the system, you can only hope it is getting better.

Results

What You'll Achieve

Expected outcomes from implementing this playbook

Knowledge-base powered chatbot with grounded answers

Accurate document Q&A with verifiable citations

A retrieval system that survives growth in the knowledge base

An evaluation harness so improvements are measurable

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Use this playbook

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The playbook is the public version. The private version is me running it for your team against a real deadline. If you have a project on the line, that is usually the faster path.

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