Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.transluce.org/llms.txt

Use this file to discover all available pages before exploring further.

  • Ask questions whose answers are evident from your transcripts. Language models are more liable to hallucinate if you ask questions that are unanswerable without access to data that’s not in your collection or details of your infra.
  • Ask for data instead of telling the coding agent to validate a hypothesis. For example, if you ask an LLM to compare two traces, it is likely to generate a difference even if that difference is not valid. One way to guard against that is to avoid prompts like “does this model spend less time exploring files before implementing?” and instead have the LLM evaluate time spent exploring on different transcripts, and then look at the resulting chart yourself to make a determination.
    • Asking the language model to compare two models is helpful for proposing hypotheses, not validating them.
  • Modify your analysis to manage long context. Try the following when working with long transcripts or many transcripts.
    • Random sampling: Randomly select a few transcripts if you are working with many of them.
    • Recursive summarization: Instruct the coding agent to summarize the content of each transcript. Then, cluster the resulting summaries instead of clustering the transcripts themselves. If your data contains a sensible pivot key, you can instruct the agent to group by that key (e.g., group by task and summarize).