Abstract Algorithms

evaluation

4 articles across 4 sub-topics

LLM Software Development Pitfalls: What to Avoid and When to Simplify

TLDR: Most bad LLM products do not fail because the model is weak. They fail because teams wrap a maybe-useful model in too much architecture: prompt spaghetti, no eval harness, weak tool schemas, huge context windows, agent chains nobody can explain...

Apr 17, 2026•19 min read

Deepeval(1)

LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)

TLDR: 📏 Traditional ML metrics (accuracy, F1) fail for LLMs because there's no single "correct" answer. RAGAS measures RAG pipeline quality with faithfulness, answer relevance, and context precision. DeepEval provides unit-test-style LLM evaluation....

Mar 29, 2026•15 min read

Ai Agents(1)

AI Architecture Patterns: Routers, Planner-Worker Loops, Memory Layers, and Evaluation Guardrails

TLDR: A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation. Those layers determine safety, latency, cost, and traceability far more than model choice alone. TLDR: Prod...

Mar 13, 2026•13 min read

Agent-architecture(1)

LLM Skill Registries, Routing Policies, and Evaluation for Production Agents

TLDR: If tools are primitives and skills are reusable routines, then the skill registry + router + evaluator is your production control plane. This layer decides which skill runs, under what constraints, and how you detect regressions before users do...

Mar 12, 2026•14 min read