RAG

How to design an internal AI assistant that helps staff find policies, procedures, and operating knowledge without creating a guessing machine.

How to build a lightweight retrieval-augmented knowledge tool with grounded answers, source citations, narrow scope, and a realistic MVP.

A business-friendly explanation of retrieval-augmented generation and why it matters when your AI must work from company knowledge.

TL;DR for operators A sustainability analytics team can receive an AI-generated answer that addresses its question and follows a retrieved corporate disclosure, yet still lacks the information needed for accurate reporting. A benchmark of seven open-source language models for ESG question answering confirms that these qualities must be evaluated separately.1 Answer relevancy ranged from 0.760 to 0.881, while factual correctness remained between 0.387 and 0.449. Different models led the three principal generation metrics. ...

TL;DR for operators An agent can already have the information needed to answer a question and still produce the wrong answer. Diehl and Lim’s Evidence-Inference Reconstruction (EIR)1 addresses that failure mode by storing retrieved source sentences separately from the agent’s evolving interpretations, then generating the scored answer in a fresh call that sees only the original question and accumulated evidence. ...

TL;DR for operators A retrieval system can improve while its benchmark understates the improvement. A generated answer can remain consistent with its evidence while a lexical-overlap metric gives it a poor groundedness score. That is the central operational lesson from Morales, Hegde, and Syed’s urban-discourse system.1 Dense semantic retrieval beat TF-IDF across every reported held-out retrieval metric, including Precision@10 of 0.660 versus 0.545. But when the dense system’s actual retrieved chunks were rated directly, its Precision@10 was 0.94. The evaluation unit changed, and so did the apparent quality of the same retriever. ...

TL;DR for operators REPAIR suggests that the next improvement to a specialized retriever does not necessarily require a larger model or a much larger synthetic dataset. The paper instead concentrates training effort on queries where the current retriever has difficulty separating a known positive from its strongest competitor, identifies the concepts associated with those failures, and generates new supervision only after grounding it in external scientific evidence. ...

TL;DR for operators A semantic-search service may want different retrieval budgets for different workloads: a cheap first-stage filter, a moderate-cost interactive search path, and a higher-quality path when more compute is available. Maintaining a separately encoded corpus for each operating point adds storage, indexing, and deployment complexity. Matryoshka Hash Representations (MHR) address that problem with one nested binary document code whose 8-, 16-, and 32-byte prefixes are independently searchable.1 The crucial detail is that these prefixes are not obtained by simply chopping bits off a conventionally trained 256-bit hash. The authors first train the 256-bit representation on MS MARCO, freeze it, and then train residual adaptors to reorganize the prefixes while preserving the full-width solution. ...

TL;DR for operators A policy-aware LLM has to do more than retrieve the right document. It must know what evidence is mandatory, when evidence is insufficient, when a case requires human review, which policy version governed the decision, and what information must be retained so the decision can later be reconstructed. Policy-as-Skill: Governed LLM Decision Support with Evidence, Deterministic Control, and Audit1 turns those obligations into explicit runtime components rather than leaving them inside prompts or application glue code. ...

TL;DR for operators A production voice agent has to decide more than what to say. It has to decide whether a pause means the user finished speaking, whether response pacing should change, which earlier constraints still matter, and how much historical audio is worth sending back to the model. Scalable Context Orchestration for Serving LLMs Over Voice1 treats those decisions as a separate systems layer. Its llmovoice middleware explicitly tracks semantic, speech-delivery, and environmental state; organizes completed interactions into persistent memory structures; and projects only a bounded, relevance-weighted portion of history into the serving model. ...