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Explore our latest articles and insights about Data Processing.
13 posts in total
How to strip names, roles, and demographics from queries before retrieval to reduce RAG bias. The redaction pipeline and the 3 leakage traps to avoid.
How to test a RAG pipeline like real software. Unit, integration, and eval tests that catch regressions before they ship. The 3-layer test strategy.
How LLM-powered query rewriting fixes vague user questions before retrieval. The prompt, the multi-query fan-out, and when rewriting hurts more than helps.
How to filter irrelevant retrieved chunks with a cheap LLM call before the final answer. The prompt, the batch pattern, and the 40 percent noise reduction.
How to pick the right k value for your RAG retriever. The 3-step tuning process, the failure modes of k=3 and k=20, and the sweet spot in between.
How to combine multiple vector stores in one RAG pipeline. The merge pattern, the deduplication rule, and when multi-source beats a single index.
How to use FAISS for production RAG. Index types, persistence, memory trade-offs, and the 4 settings that decide if FAISS beats a managed vector DB.
How to wire eval pipelines into CI so every agent change is scored automatically. The nightly job, the regression gate, and the dashboard that matters.
How to load evaluation metrics dynamically in a Python eval pipeline. The registry pattern, entry points, and the test override that makes CI fast.
Why LLM judges without explicit reasoning drift, and how chain-of-thought rationales make their scores defensible. The prompt, the parser, the trust.
How to build an LLM-as-a-judge evaluation framework for agentic AI. The prompt, the rubric, the bias controls, and the loop that catches regressions.
How RecursiveCharacterTextSplitter works, why it beats naive chunking, and the separator order that makes or breaks retrieval quality.