The real power of LangChain (or Langsmith) lies in its robust evaluation and experimentation capabilities, which often go overlooked amid other featur...Show more

Use an LLM as a judge to compare outputs from multiple experiments or models, facilitating side-by-side evaluation and selection of the best performin...Show more

Integrate cloud code with Langsmith via mcp to run experiments, inspect traces, modify code, and rerun in a tight feedback loop for rapid AI developme...Show more

Use evaluator hooks on GitHub to automatically run AI evaluations on each commit, enabling continuous feedback on prompt performance.

Interfaces and SDKs provided by OpenAI to run prompt experiments, evaluate outputs, and integrate LLM evaluations into workflows.

Anthropic’s new Haiku language model, suggested for comparative experiments against other LLMs like GPT5 mini.

An experimental miniature version of GPT-5 usable in the Playground, Studio links, or via SDK for prompt performance comparisons.

A tool (aka LLM arenas) for conducting pairwise comparisons between model responses, enabling automated choice of better outputs.

OpenAI’s API-based evaluation framework offering out-of-the-box LLM judge evaluations to score and compare model outputs automatically.

A public Hugging Face dataset containing ChatGPT-style prompts (e.g., act as an Ethereum developer) for testing and evaluating language model outputs.

Adopt an iterative, experiment-driven approach to prompt engineering and model evaluation to systematically improve performance and gather rapid feedb...Show more

Implement precise data passing into the model’s context window, including formatting, conversions, and calculations, to improve prompt reliability.

Maintain a curated dataset of prompts, expected outputs, and evaluation criteria to continuously test model upgrades and prompt engineering workflows.

Set up pairwise experiments where an LLM compares two nondeterministic outputs and declares which is better to guide prompt improvements.

Use OpenAI’s evaluation framework to have an LLM judge outputs and assign a numeric score (e.g., 1–100) as an automated quality metric.

Leverage Hugging Face’s ChatGPT prompt dataset to run experiments on diverse prompt examples and build automated evaluators.

View agent engineering through the same lens as traditional software development by applying known testing paradigms for maintainability and clarity.

Optimize AI outputs not only for accuracy but also for cost or other business metrics by iterating on prompts and workflows against reference datasets...Show more

Iteratively test different app versions, prompts, data formats, or AI models against a gold standard dataset to benchmark accuracy and optimize outcom...Show more

Leverage traditional software testing frameworks like unit and regression tests to systematically evaluate AI agent performance using reference inputs...Show more