Engineering Manager / Technical Lead
A topic hub collecting every article tagged Evaluation. Use it to explore related posts and follow this theme across the site.
17 articles
Stop arguing about whether AI answers are too long. Human reading and review capacity are measurable, so the ceiling is derivable: 238 words per minute, a time budget per response class, and a token number you can put in a system prompt and an eval.
Frontier labs increasingly claim their models understand intent with less prompting. Here is what the evidence supports, what training changed, and how to test ask-versus-guess behavior in your own agents.
Build a small Claude model router with explicit task classes, Low effort, bounded tools and budgets, isolated workspaces, acceptance tests, and cost-per-result evaluation.
A practical architecture for math tutoring agents: generate random problems with seeded templates, compute ground truth outside the LLM, use the model for explanations, and verify every answer before showing it to learners.
A source-backed guide for engineers and PMs on shaping model behavior and personality across frontier LLMs and small language models, from prompts and DPO to persona vectors and distillation chains.
A grounded, motivating guide for students, juniors, switchers, and PMs on why CS fundamentals still matter, how AI skills layer on top, and what to build for the 2026 job market.
A practical guide for engineers and PMs designing RAG apps over well-structured, chained knowledge: how to chunk, retrieve, rerank, cite, evaluate, and improve with evidence instead of vibes.
A source-backed deep dive for engineers and PMs on building recommendation engines inside AI-agent products, from MVP heuristics to retrieval, ranking, evaluation, safety, observability, and production launch.
Most AI programs start with intuition and optimism, then hit a harder question: can anyone prove the system is creating value? This guide turns AI adoption into a measurement problem, using baselines, outcome metrics, evals, and learning loops.
A source-backed comparison of Agno's latest native eval docs and LangWatch Scenario's simulation-based testing model, with practical guidance on when to use each and how to combine them.
There is no truly bulletproof system prompt. But there is a practical engineering standard for making prompts far more robust across Sonnet, Haiku, GPT, and reasoning-style models.
Most AI code review bots fail for a simple reason: they optimize for visible comments instead of reviewer trust. This guide pulls together current benchmarks, practitioner reports, product limitations, and design patterns for building a code review agent that is fast, quieter, less biased, and less hallucination-prone.
A practical, source-backed workflow for reviewing AI-generated responses for factual accuracy and relevance, scoring them with structured rubrics, and turning feedback into better prompts, evals, and product decisions.
A practical guide for implementing LangWatch evaluations in Agno and AgentOS systems, from first traces and batch experiments to structured-output scoring, production monitors, and background evaluation hooks.
LLM-as-a-judge can be one of the most useful patterns in agent evaluation, but only if you understand where it breaks: order bias, self-preference, verbosity bias, weak judges, and evidence-free scoring. This guide explains the pattern, the common traps, and the fixes that make it practical.
Why production agent evaluation is moving beyond output-only checks, how trace-aware grading complements scenario testing, and how LangWatch, LangSmith, and Langfuse compare.
Unit tests tell you if your code works. Scenario tests tell you if your agent behaves. But how do you measure quality across hundreds of examples and track it over time? LangWatch evaluations fill that gap.
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