Be Confident in your LLM products

Chatter is a single platform to build, evaluate, and version LLM deployments.

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Build and test LLMs with your whole team

Build confidence in your LLM products by bringing in all stakeholders and iteratively testing your models. View differences on key metrics.

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Jinja2 templating engine

Perform intermediate data transformations right in your prompt, using Jinja2.

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Retrieval Augmented Generation (RAG)

In seconds, set up your RAG pipeline, whether from a vector DB or other data source. Build and maintain document repositories.

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SDK & Code Export

Separate your iteration from your codebase, for maximum speed and prompt security.

CREATE

Complex, configurable
chains

Build complex chains using a variety of models and configurations. Function calling, chaining, data manipulation, and more supported out of the box. Integrate your Chatter chains with code in seconds.
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API Key Vault

One place to keep all your LLM API keys. Manage token usage and costs over time or even per call.

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Analytics

Get insight into every call. Know how long they take, how many tokens are used, how much they cost, how they perform and more.

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Observability

Look into every call within a chain. Know exactly how information is being passed down and debug fast.

TEST

Automatic evaluations and prompt versioning.

Build and maintain a testing suite, with automatic evaluation across almost a dozen commonly used metrics including LLM-based evaluation, semantic similarity, and regex matching. Keep everything versioned and logged in one place for each comparisons.
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Function builder

A dead-simple interface to build out and maintain a library of function calls. Plus, easy configurability per API call.

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Routing

Have complex routing flows involving multiple sets of functions and system prompts for a single query? We've got you covered.

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Chat testing

Have chats you need to test at specific points? We support multiple roles within a call, give you a seamless chat import, and let you easily run evaluations on specific messages.

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Collaboration and additional non-technical interface.

Build and maintain a testing suite, with automatic evaluation across almost a dozen commonly used metrics including LLM-based evaluation, semantic similarity, and even basic regex matching. Keep everything versioned and logged in one place for each comparisons, with a separate viewer for non-technical users.

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