Document Processing
- [ ] Define chunking strategy (semantic vs fixed-size)
- [ ] Handle multiple file formats (PDF, DOCX, HTML, Markdown)
- [ ] Strip irrelevant content (headers, footers, navigation)
- [ ] Preserve document structure where semantically important
- [ ] Extract and store metadata (source, date, author, section)
- [ ] Set up document versioning strategy
Embedding & Indexing
- [ ] Choose embedding model appropriate to domain
- [ ] Benchmark embedding quality on representative queries
- [ ] Set up vector database with appropriate index type
- [ ] Store both dense and sparse vectors for hybrid retrieval
- [ ] Implement incremental indexing for new documents
Retrieval
- [ ] Implement hybrid retrieval (dense + sparse)
- [ ] Set up re-ranking with a cross-encoder
- [ ] Implement metadata filtering
- [ ] Benchmark retrieval latency under load
- [ ] Implement fallback for zero-result queries
Generation
- [ ] Design system prompt for your use case
- [ ] Implement citation in responses
- [ ] Handle "I don't know" gracefully
- [ ] Test with out-of-distribution queries
Evaluation
- [ ] Build evaluation dataset (50+ query-answer pairs)
- [ ] Implement retrieval metrics (recall@k, MRR)
- [ ] Implement generation metrics (faithfulness, relevance)
- [ ] Set up automated evaluation in CI/CD
Monitoring
- [ ] Log all queries and responses
- [ ] Track latency percentiles (p50, p90, p99)
- [ ] Monitor token usage and cost per query
- [ ] Alert on error rate spikes