Results for “LangSmith”

Eval platforms (Braintrust, LangSmith, Weights & Biases) accelerate teams. The buy-vs-build call depends on team size, use cases, and customization needs.

Eval platforms (Braintrust, LangSmith, Weights & Biases) all support evaluation differently. Selection matters.

LLM observability tools (LangSmith, LangFuse, Helicone, Datadog LLM, custom) all trace conversations. The differentiation is in evaluation, dashboards, and alerting — and choosing the wrong tool wastes months.

Building a unified view across LangSmith, Datadog LLM Observability, OpenTelemetry, and custom dashboards.

Compare LangSmith, Braintrust, Humanloop and friends for evaluating multi-step agent traces.

LangGraph became the production favorite in 2026 for good reasons — explicit state, checkpointing, first-class MCP. Build a real agent end-to-end and learn why.

Numbers on leaderboards are seductive and often wrong. Learn the big benchmarks, their leaderboard positions, their recently-exposed cheats, and how to run your own evals.

A prototype agent and a production agent have the same LLM. What's different is everything around it — durable state, retries, idempotency, observability. The real engineering.

Everything comes together. Design, code, test, secure, and ship a production-quality agent with open-source code you can fork today.

A trace is the full record of what an agent did and why.

Fine-tune, evaluate, serve, monitor. The ML engineer is the person who ships the models that now power medicine, law, and design. It is the highest-leverage engineering role.

You can't improve what you don't measure. Build an eval set, pick metrics, and turn prompt engineering from gut-feel into a rigorous discipline.

AI monitoring requires more than uptime metrics. Quality monitoring, drift detection, and outcome tracking are the differentiation.

Which model when? Claude, GPT, Gemini, Grok — and how to choose. 578 lessons.

Every family in the industry. Variants, strengths, limits, pricing. 357 lessons.

Prompt engineers design and tune instructions for AI systems. It didn't exist before 2022 — now it's a core role inside every AI team.

Testing a model's quality — beyond benchmarks, using real tasks and user feedback.

MLOps specifically for LLM-based applications — prompt versioning, eval, and inference ops.

Open-source framework for chaining LLM calls, tools, and memory into apps.