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Production AI

Explore our latest articles and insights about Production AI.

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AI Bytes

Practical insights for production AI systems

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.

RAG Guardrails Data Processing+2 more

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9 min

AI Engineering in Practice

pip vs uv vs poetry for Python AI services

Which Python dependency manager should you use for production agent services in 2026? The install speed, lockfile story, and Docker build times compared.

Python AI Agents Production AI+2 more

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9 min

AI Engineering in Practice

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.

AI Agents Error Handling Resilience+2 more

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8 min

LLM Engineering

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.

Evaluation LLM Metrics+2 more

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8 min

LLM Engineering

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.

RAG Evaluation Metrics+2 more

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9 min

LLM Engineering

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.

RAG Evaluation LLM+2 more

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8 min

LLM Engineering

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.

RAG Evaluation Production AI+2 more

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8 min

LLM Engineering

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.

RAG LLM Evaluation+2 more

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8 min

LLM Engineering

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.

RAG LLM Data Processing+2 more

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8 min

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Production AI

AI Bytes

Query anonymization for RAG bias mitigation

pip vs uv vs poetry for Python AI services

Retry patterns for LLM API errors in production

Choosing the LLM judge for evaluation pipelines

Ground truth vs relevancy in RAG evaluation

Hallucination testing for RAG pipelines

Testing and evaluating RAG pipelines end to end

Fact-checking RAG answers: grounding with verification

LLM-based content filtering for RAG pipelines

Weekly Bytes of AI

Production AI

AI Bytes

Query anonymization for RAG bias mitigation

pip vs uv vs poetry for Python AI services

Retry patterns for LLM API errors in production

Choosing the LLM judge for evaluation pipelines

Ground truth vs relevancy in RAG evaluation

Hallucination testing for RAG pipelines

Testing and evaluating RAG pipelines end to end

Fact-checking RAG answers: grounding with verification

LLM-based content filtering for RAG pipelines

Weekly Bytes of AI