Use Azure's evaluator library for continuous monitoring and evaluation of prompts similar to Langsmith's approach.

Cameron built a basic pipeline using an evaluator that overrides rows in an existing dataset for multiple experiments, tracking correctness rates, run...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.

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.

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

Define AI output evaluation categories as catchable, predictable, or random to tailor your testing and feedback processes in domain-specific data pipe...Show more

Follow the LangChain Academy curriculum—starting with tracing, then testing and evaluation in Langsmith, and finally advanced prompt engineering—to sy...Show more

Adopt an AI engineering or agent engineering practice with offline, online, and real-time test optimizations to continuously gather feedback and refin...Show more

Build a backtesting framework using Langsmith to evaluate existing AI features, run model upgrade tests, and track performance changes over time.

Feed real-time market data from the API into LangGraph and configure event alerts to trigger agent workflows.

Use Deep Agents in LangGraph to encapsulate and optimize high-frequency trading strategies.

Convert traditional if-else for-loop trading logic into a LangGraph agent-based cognitive architecture embedding decision trees and sub-agents to impr...Show more