Why do you need UpTrain?

After coming up with an LLM application, your first step is to find the best LLM for your use case. While there are thousands of LLMs available, each with its unique capabilities, no single LLM can be universally deemed as the best in all cases. Choosing the best LLM (Language Model) for your needs involves considering several factors, including formulating an effective prompt. The prompt plays a crucial role in eliciting accurate and relevant responses from the model.

The effectiveness of an LLM depends on how well it meets your specific requirements and provides valuable results for your use case. It is crucial to consider factors such as the model’s training data, its knowledge base, and its ability to comprehend and generate coherent responses.

To assist you in identifying the most suitable LLM and prompt combination, UpTrain offers a comprehensive framework for evaluating and selecting LLMs based on their performance on specific tasks. UpTrain helps in determining which LLM performs optimally when paired with a well-crafted prompt, considering metrics such as accuracy, relevance, and the overall quality of responses.

By leveraging UpTrain, you can experiment with different prompts and evaluate their effectiveness with various LLMs. This iterative process allows you to refine your prompts and identify the most effective ones that yield desired outcomes from the selected LLMs.

How does UpTrain work?

Using UpTrain to evaluate LLMs and prompts is like building blocks. Each new block is built on top of the previous one, and the process continues until you have your own custom LLM pipeline.

There are six main components in UpTrain:

  1. Evals
  2. Operator
  3. Chart
  4. Check
  5. CheckSet
  6. Settings