Prompt Optimizer
Writing prompts by hand is slow and tedius. Use our prompt optimizer (free to try) to automatically generate optimized prompts for your LLM.
Save 80% of manual prompt engineering effort.
The fastest way to make your prompts, RAG and AI agents more reliable.
Iterate faster during development and prevent regressions in production.
Multi-step RAG & AI agents are hard to get right. A small change in prompts, function call definitions or retrieval parameters can cause large changes in final LLM output.
Our vision is helping product and engineering teams build self-improving AI-systems that require minimal human oversight.
Writing prompts by hand is slow and tedius. Use our prompt optimizer (free to try) to automatically generate optimized prompts for your LLM.
Save 80% of manual prompt engineering effort.
Leverage our curated adversarial datasets aimed at testing your AI app's robustness against prompt-injection attacks.
Curate golden datasets with built-in versioning.
Test your pipeline's performance on each dataset using our collection of in-house scores that measure accuracy, tone, hallucinations, precision and recall.
We create custom evals unique to your use-case aligned with your preferences.
We go beyond passive monitoring. We actively track and score how users are using your AI app in production and flag cases that need your attention.
Easily convert traces into test cases and add them to your golden dataset.
RAG systems can fail during retrieval or reasoning. Use our RAG optimized scores to quickly identify bottlenecks in your pipeline.
Function calling is hard. We make it easier to stress-test function calling in your agents.
Easily annotate examples by hand when human supervision is necessary.
Share experiment results and production traces with your team.
We're experts in supporting companies tackling high-stakes domains where getting the wrong answer leads to high churn or regulatory consequences.
Our customers use Hamming to build retentive AI products in their industry.
Building reliable AI products is a team effort. Hamming is built to support cross-team collaboration.
For each experiment, track your hypothesis, proposed changes and learnings.
Override AI scores. Every override aligns the AI judge with your preferences.
Any change to the golden dataset triggers a new version. Teams can compare experiment results on an apples to apples basis.
Search across all traces and quickly root-cause why your AI system produced a particular answer.
AI pipelines are non-deterministic. You can run multiple runs for the same experiment - visualize performance distributions and isolate flake tests.
Share datasets, experiment results and traces with teammates.