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Six data vendors moved on the same layer for AI agents in one month. Who signs off what the data means?
NVIDIA’s Auto Ontology got its first write-ups at the end of October. Free, open source. It sits between an AI agent and your data warehouse and tells the agent what […] The post Six data vendors moved on the same layer for AI agents in one month. Who signs off what the data…
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LLM Observability: The Production View of the Harness
LLM observability is just observability applied to a new kind of system. The signals come in the same three shapes a serious engineering team has shipped for two decades (traces, […] The post LLM Observability: The Production View of the Harness appeared first on 7wData .
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Audit Trails for LLM Applications: What to Log, What to Retain, What Regulators Expect
I sat in on an incident review last quarter that I keep coming back to. A customer-facing assistant had quoted a refund policy the company did not have. The model […] The post Audit Trails for LLM Applications: What to Log, What to Retain, What Regulators Expect appeared first…
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Data Integrity for LLM Systems: The Upstream Dependency Nobody Owns Yet
The first time I asked a room full of LLM operators which person on their team owned the integrity of the retrieval corpus, the answer came in three flavours, all […] The post Data Integrity for LLM Systems: The Upstream Dependency Nobody Owns Yet appeared first on 7wData .
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LLM-as-Judge: When One Model Evaluates Another, and When Not to Trust the Verdict
The cheapest evaluator you will ever deploy is another language model, and the cheap thing is also the one that lies to you in the most flattering way. I have […] The post LLM-as-Judge: When One Model Evaluates Another, and When Not to Trust the Verdict appeared first on 7wData .
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Wikidata and External Knowledge Graphs as Ground Truth for LLM Output QC
The first time I ran a model output through a Wikidata check I felt the same small joy I get from a well-tuned guitar. The model had cheerfully informed me […] The post Wikidata and External Knowledge Graphs as Ground Truth for LLM Output QC appeared first on 7wData .
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When a Hallucination Is a Design Constraint, Not a Bug
A memory leak is a property of how programs are. A race condition is a property of how concurrent programs are. A cosmic-ray bit-flip is a property of how dense […] The post When a Hallucination Is a Design Constraint, Not a Bug appeared first on 7wData .
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Reducing hallucinations: what the response layer actually does
The first time I watched a team try to “fix hallucination” with a single technique, they spent three months tuning a retrieval pipeline and shipped a system that hallucinated almost […] The post Reducing hallucinations: what the response layer actually does appeared first on…
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A Taxonomy of Hallucinations: Intrinsic, Extrinsic, and the Systemic Kind
You cannot mitigate a failure mode you cannot name. That sentence sounds obvious until you sit through a post-incident review for a hallucinated answer, and watch four engineers describe the […] The post A Taxonomy of Hallucinations: Intrinsic, Extrinsic, and the Systemic Kind…
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Continuous Evaluation: When the Benchmark Is Your Production Traffic
The held-out test set was a useful fiction. It got us through the first wave of putting language models into real workflows; everyone needed something to point at and say […] The post Continuous Evaluation: When the Benchmark Is Your Production Traffic appeared first on 7wData .
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Evaluating RAG Systems: Faithfulness, Answer Relevance, Context Precision
The first useful thing to say about evaluating a retrieval-augmented system is that you are not evaluating a model. You are evaluating a pipeline. The model is one component in […] The post Evaluating RAG Systems: Faithfulness, Answer Relevance, Context Precision appeared first…