LLM Engineering9 min read
Query anonymization for RAG bias mitigation
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.
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62 posts in total
AI Engineering in Practice9 min read
pip vs uv vs poetry for Python AI services
Which Python dependency manager for production agent services? Install speed, lockfiles, and Docker build times of pip, uv, and poetry compared.
AI Engineering in Practice8 min read
Retry patterns for LLM API errors in production
How to build retry logic that handles rate limits, timeouts, and transient failures without burning money. The backoff rules and the 3 errors you must not retry.
LLM Engineering8 min read
Choosing the LLM judge for evaluation pipelines
How to pick the LLM that grades your LLM. The cost-quality tradeoffs, the calibration check, and why a weaker judge is sometimes the right call.
LLM Engineering9 min read
Ground truth vs relevancy in RAG evaluation
Why ground truth and relevancy measure different things in RAG evals. When to use each, how to build both datasets, and the 2 metrics that matter most.
LLM Engineering8 min read
Hallucination testing for RAG pipelines
How to test a RAG pipeline for hallucinations systematically. Adversarial prompts, the out-of-scope set, and the metric that catches confabulation.
LLM Engineering8 min read
Testing and evaluating RAG pipelines end to end
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.
LLM Engineering8 min read
Fact-checking RAG answers: grounding with verification
How to fact-check RAG answers with a second LLM pass that verifies every claim against the retrieved context. The prompt, the rejection rule, and the loop.
LLM Engineering8 min read
LLM-based content filtering for RAG pipelines
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.
LLM Engineering8 min read
Retriever k-value tuning for RAG: the right top-k
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.
LLM Engineering8 min read
Combining vector stores in RAG: multi-source retrieval
How to combine multiple vector stores in one RAG pipeline. The merge pattern, the deduplication rule, and when multi-source beats a single index.
LLM Engineering8 min read
FAISS vector stores in production RAG
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.