Improve your

LLM applications

in production

Optimize LLM applications by tuning prompts and models

App screenshot

AI-powered LLMOps for developers

From development to production across data management, evals & fine-tuning.

Prompt Engineering Copilot

Use our Prompt Engineering Copilot to get to more accurate prompts faster via AI-powered tuning of prompts and models

Evaluate

Integrate Log10's llmeval tool to iterate even faster during development & continuously monitor the accuracy of your LLM apps in production

AutoFeedback

Scale human review of LLM outputs with the power of Log10's AutoFeedback solution. Read the technical details

Debug, compare and improve prompts & models

Logs

Stats
Get latency, cost & stats for each request
Feedback
Collect feedback for model fine-tuning
Organize
with full text search, tags and filters
Create playgrounds from logs
improve accuracy with new prompts and models
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Metrics

Operational
Summary metrics on costs, usage and SLA
Accuracy
Track accuracy of completions (coming soon)
Metrics screenshot

Playgrounds

Compare
Compare in one view prompts from OpenAI and Anthropic.
Debug
Integrated with logging and tracing for fast debugging
Collaboration
Build for multi user collaboration from the start
OpenAI & Anthropic
Configure and connect to model vendors in one place including to your fine-tuned models
AutoPrompt
Get to the perfect prompt faster with AI-powered prompt tuning
Playgrounds screenshot

Evaluations

llmeval
GitHub CI/CD app and cli to systematically test prompts with metric, tool, and model-based evaluations
AutoFeedback
Scale human feedback with custom evaluation models
Evaluations screenshot

Integrate Log10 with a single line of code

Easy programmatic integration

Just call log10(openai) and use the OpenAI client library as before

openai.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 import openai from log10.load import log10 log10(openai) client = openai.OpenAI() completion = client.chat.completions.create( model="gpt-3.5-turbo", messages=[ { "role": "system", "content": "You are the most knowledgable Star Wars guru on the planet", }, { "role": "user", "content": "Write the time period of all the Star Wars movies and spinoffs?", }, ], ) print(completion.choices[0].message)

Interested in managing LLM accuracy at scale?