Topic
Debugging Hallucinations & Output Quality
14 posts on debugging hallucinations & output quality — part of developer tools on the n4n AI blog.
Why longer context windows can increase hallucinations
Why expanding prompt size raises the long context window hallucination rate: analysis with code for retrieval limits and a decisive engineering takeaway.
Why GPT-4o hallucinates citations and how to catch it
GPT-4o invents citations because it optimizes for fluent text, not verified provenance. Learn deterministic ways to detect and block hallucinated references.
Why Claude and GPT-4o hallucinate differently on math
A head-to-head engineer's comparison of claude vs gpt-4o hallucination math patterns, cost, latency, and ergonomics for production LLM apps.
Using temperature settings to reduce hallucination rate
Practical steps to tune LLM temperature settings and measure hallucination reduction, with runnable eval code and production guardrails.
Using self-consistency checks to reduce hallucinations
Learn how to implement a self-consistency check llm hallucinations pipeline with sampling, voting, and verification to cut errors in production systems.
Prompt patterns that reduce hallucination in JSON output
Practical prompt patterns to reduce JSON hallucination in LLM outputs: schema contracts, few-shot guards, validation loops, and cross-model testing.
Measuring output quality drift after a model upgrade
Practical how-to for measuring output quality drift model upgrade: capture prompts, run side-by-side evals, score drift, and gate deploys with thresholds.
Logging LLM outputs to catch quality regressions early
Practical steps to implement logging llm outputs quality regression detection in production, catching model drift and hallucinations before users do.
How to spot hallucinated code in LLM pull requests
A practical workflow for engineers to detect and verify hallucinated code in LLM pull requests before merge, using static analysis and tests.
How to detect hallucinated function calls in tool use
Practical steps to detect hallucinated function calls in LLM tool use, with schema validation, logging, and verification code for engineers building agents.
Grounding LLM outputs with RAG to cut hallucinations
A practical guide to implementing RAG grounding reduce hallucinations: chunking, hybrid retrieval, prompt design, citation checks, and eval loops for engineers.
Debugging inconsistent outputs from identical prompts
Practical steps to diagnose and fix inconsistent llm outputs same prompt, covering temperature, seeds, provider drift, and verification.
Building an eval set to catch hallucinations before deploy
A hands-on tutorial for building an eval set to catch LLM hallucinations before deploy, with runnable code for generating, scoring, and automating tests.
A debugging checklist for LLM hallucinations
A practical llm hallucination debugging checklist for engineers: step-by-step items to isolate, reproduce, and fix false outputs from language models.
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