Covers hybrid retrieval (vector + BM25 with rank fusion), knowledge graph integration, semantic/AST-based chunking, multi-stage reranking pipelines, domain-specific RAG for code/SQL/legal/medical, evaluation without ground truth labels, agentic self-correction, and production observability.

118 pages, 16 chapters, free PDF. Happy to discuss any of the architectural trade-offs. Particularly interested in feedback on the hybrid retrieval section (Ch 2) and evaluation frameworks (Ch 11).

Recent updates:

- Outputs anchored to uploaded knowledge bases

- ~90% adherence to source material observed

- 1.78× faster dataset creation (1,000+ rows in ~10 mins)

- Edit columns before/during/after runs

- Better diversity beyond 5,000 rows

- SOP uploads converted into structured evaluation scenarios

- One-click synthetic variable generation for prompt testing

For teams evaluating LLM systems under data constraints, this has reduced iteration friction significantly.

Curious how others are validating grounding + diversity at scale.

Because you stop measuring.

Expert intuition works on 10 examples. It doesn’t generalize to 10,000 inputs and three interacting failure modes.

When you optimize by feel:

-results aren’t reproducible

-changes aren’t versioned

-trade-offs aren’t quantified

-regressions slip in silently

This isn’t a prompting problem. It’s an optimization problem.

Treat prompts like hyperparameters.

Dataset → Evaluator → Optimizer → Ranked prompts.

Once you introduce an objective function, intuition becomes optional.

We wrote a cookbook that lays out the full workflow step by step for teams moving beyond manual iteration.

We’ve added native image rendering inside Datasets and Prompt Workbench so generated images appear inline, next to the prompts that produced them.

This allows:

Faster output review

Easier comparison across runs

Tighter iteration loops without context switching

Curious how others are handling evaluation and iteration for multimodal pipelines today.

In vision-language systems, hallucination often means inventing objects, attributes, or actions that are not present in the image at all.

Examples:

- Describing people who don’t exist - Inferring actions that never occurred - Assigning attributes unsupported by visual evidence

As these models are increasingly used for e-commerce listings, accessibility captions, document extraction, and medical imaging, the consequences escalate quickly.

Most evaluation pipelines are still text-centric. They don’t verify whether the generated description is actually grounded in the image.

Detecting image hallucination requires multimodal evaluation that reasons over both the image and the output jointly.

Curious how teams here are approaching hallucination detection for vision-language models today.

Comments URL: https://news.ycombinator.com/item?id=46508362

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PMs and engineers would run large batches of tests, then immediately jump into the workflow graph to replay calls and figure out one thing:

Where exactly did the agent’s path diverge from the intended flow?

The only way to answer that was manual flow tracing — stepping through nodes, comparing expected vs actual paths, and trying to spot the turn where behavior shifted. It was slow but necessary work.

We turned that repeated behavior into a feature called Flow Analysis.

For each test run, Flow Analysis computes and surfaces:

- The exact path the agent took through the workflow - The node where it diverged from the expected path - How the rest of the conversation evolved after that point

This makes debugging more of an engineering task (fix the specific node/logic/prompt) instead of an investigation across the whole graph.

If you’re working with voice agents or complex conversational flows and still manually scanning graphs to debug failures, would be interested in your thoughts on this approach.

More details: https://app.futureagi.com/dashboard/simulate/agent-definitio...

Comments URL: https://news.ycombinator.com/item?id=45883590

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What it is

Automatic error clustering for AI agents

Symptom → likely root cause mapping

Actionable fix suggestions you can validate with a focused eval loop

Why it’s different

You debug categories of failures, not one-off traces

It ranks hypotheses (e.g., threshold too high, retrieval drift, prompt regression, guardrail friction)

It proposes small, surgical changes you can A/B and roll back quickly

How it works (high level)

We instrument LLM calls, tool invocations, retrieval hits, guardrail events, and outputs as spans (OpenTelemetry compatible)

We build semantic signatures of failure states and cluster them

We label clusters and map them to ranked hypotheses using a mix of rules + learned patterns from historical fixes

We attach a minimal eval set per cluster so you can confirm the fix without re-running your whole suite

Comments URL: https://news.ycombinator.com/item?id=45330720

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=45186499

Points: 1

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Inside, you’ll find practical insights on chunking methodologies, re-ranking systems, embedding techniques, hallucination control, RAG implementation, evaluation strategies, plus countless additional topics.

You’ll also learn:

- Frameworks to reduce hallucinations - Enterprise evaluation practices - ROI optimization via metrics

Would love feedback from this community.

Comments URL: https://news.ycombinator.com/item?id=45089432

Points: 2

# Comments: 1