RAG Pipeline Architecture

MicrocosmWorks implements conflict resolution in RAG pipelines through source authority ranking, timestamp-based recency weighting, and confidence scoring that evaluates how strongly each retrieved passage supports its claim. When conflicting passages are retrieved, our pipeline presents the highest-authority answer while transparently surfacing the disagreement and source citations so users can make informed decisions. We also build feedback loops where domain experts can flag incorrect resolutions, which improves the retrieval ranking over time.

MicrocosmWorks uses content-aware chunking that applies different strategies based on document structure—semantic paragraph splitting for prose, row-level or section-level chunking for tables with header context preserved, and function-level chunking for code with import statements attached. We enrich each chunk with metadata including document title, section hierarchy, and content type so the retrieval stage can apply type-specific scoring. This approach consistently outperforms naive fixed-size chunking by 25-40% on retrieval relevance benchmarks in our client projects.

MicrocosmWorks builds evaluation harnesses that test RAG pipelines across three dimensions: retrieval relevance (are the right chunks being found), answer faithfulness (does the generated answer actually reflect the retrieved content), and answer completeness (does it address the full question). We create golden test sets with domain experts that include known-answer queries, adversarial edge cases, and questions that require multi-document synthesis. This evaluation runs automatically in CI/CD so every pipeline change is benchmarked against baseline quality metrics before deployment.

MicrocosmWorks selects vector databases based on your scale, query pattern, and operational requirements—Pinecone for managed simplicity, Weaviate for hybrid keyword-vector search, pgvector for teams already invested in PostgreSQL, and Qdrant for high-throughput self-hosted deployments. At scales below 10 million vectors, most options deliver sub-100ms latency, but the differences become significant at hundreds of millions of vectors where index type, quantization, and sharding strategy matter enormously. We benchmark your actual embedding dimensions and query patterns against shortlisted options during our architecture design phase.

MicrocosmWorks builds incremental ingestion pipelines that watch source document repositories for changes, re-chunk and re-embed only the modified sections, and update the vector store without requiring a full reindex. We implement document fingerprinting that detects content changes at the section level, so a single paragraph edit does not trigger reprocessing of an entire 200-page document. For clients with real-time freshness requirements, we add a live retrieval layer that queries the source system directly for recently modified documents and merges those results with vector search hits.