RAG Tuning Checklist - RAGDebugger | Optimize Your Pipeline

📥 Data Preparation

Clean and preprocess documents

Remove duplicates, fix encoding issues, and normalize text formatting.

Define document metadata schema

Include source, date, category, and other relevant metadata for filtering.

Create evaluation dataset

Build a golden dataset of query-document pairs for measuring retrieval quality.

📦 Chunking Strategy

Experiment with chunk sizes

Test 256, 512, 1024 token chunks. Smaller for precision, larger for context.

Add chunk overlap

Use 10-20% overlap to preserve context across chunk boundaries.

Consider semantic chunking

Split on paragraph/section boundaries rather than fixed token counts.

Include parent document references

Store parent document ID for context expansion when needed.

🔢 Embedding Model

Benchmark embedding models

Compare OpenAI, Cohere, BGE, E5 on your specific domain data.

Consider domain-specific fine-tuning

Fine-tune embeddings on your corpus for specialized terminology.

Evaluate embedding dimensions

Balance between quality (higher dims) and cost/speed (lower dims).

🔍 Retrieval Configuration

Tune top-k parameter

Start with k=5-10, increase if recall is low, decrease if precision suffers.

Set similarity threshold

Filter out low-relevance results below a minimum similarity score.

Implement hybrid search

Combine vector search with BM25/keyword search for better coverage.

Add metadata filtering

Use metadata to narrow search scope (date range, category, source).

🔄 Reranking

Evaluate reranking benefit

Test if reranking improves relevance enough to justify latency cost.

Compare reranking models

Test Cohere Rerank, BGE Reranker, cross-encoders on your data.

Optimize rerank top-n

Retrieve more candidates (k=20-50) then rerank to top 5-10.

✨ Generation

Optimize prompt template

Clear instructions, context placement, and output format specification.

Add citation instructions

Instruct LLM to cite sources and indicate uncertainty.

Tune temperature and max tokens

Lower temperature for factual responses, appropriate length limits.

Implement fallback handling

Graceful responses when retrieval returns no relevant results.

📊 Evaluation & Monitoring

Track retrieval metrics

Monitor precision@k, recall@k, MRR, and NDCG over time.

Measure groundedness

Evaluate how well responses are grounded in retrieved context.

Set up failure labeling

Categorize and track retrieval failures for systematic improvement.

Implement regression testing

Automated tests against golden dataset on every change.