Isolate, measure, and optimize what's wasting tokens vs. what improves results
prune0 applies data-driven feature selection to prompt engineering, cutting costs while improving quality and latency.
The Problem
Traditional ML has feature testing. Prompt engineering still relies on guesswork about what context actually matters.
Reduce LLM budget currently wasted on ineffective context. prune0 identifies exactly which context matters, dramatically reducing API costs while maintaining or improving quality.
Transform hacky prompt & context experimentation into minutes of automated testing. prune0 eliminates the code-deploy-test loop, freeing your engineers to focus on building, not tweaking.
Bring scientific methodology to prompt engineering. Replace intuition with evidence by measuring the actual contribution of each context element, just like feature testing in traditional ML.
The Solution
prune0 brings the scientific approach of feature selection to prompt engineering. Isolate variables. Measure impact. Optimize tokens.
Automatically break down your context sources into testable slices - from chat history to vector store results to metadata - to identify what actually matters.
Test individual context elements in isolation to measure their actual contribution to response quality, just like you'd test features in a traditional ML model.
Replace guesswork with evidence. See exactly which context elements improve quality and which are just burning through your API budget.
The Process
A systematic approach to context optimization without the hacky workflows
Import your conversation logs, memory blocks, and metadata directly from your existing stack. Works with any context source - vector DBs, graph DBs, user profiles, chat history, or custom data.
Compare the same query with different context bundles - test recent interactions, semantic similarity, user metadata, and system configurations to measure which actually improve responses.
See comprehensive side-by-side analysis of token usage, response quality, and latency to identify which context elements provide value versus just increasing costs.
Implement the optimized context strategy in your production environment with our simple API or export functions. Continue testing as your application evolves.
When we first started testing, we were shocked to find that certain context we thought was essential (user profile data, system configurations) was actually hurting response quality while driving up costs. Only through systematic context testing did we discover what actually mattered.
The Impact
Stop the guesswork. Start measuring what matters.
Why prune0?
Unlike general-purpose tools like LangSmith or Weights & Biases, prune0 is built specifically for context optimization with a feature-testing approach to prompt engineering.
Cut your LLM bills starting today and join the growing list of companies optimizing their AI context strategy.
Start Optimizing Your Prompts