You can't improve what you don't measure
The single platform to accelerate development of AI apps and agents — then perfect them in production.
BotDojo provides end‑to‑end tooling for tracing, experiment management, prompt iteration, online/offline evals, and production‑grade observability. Build confidently and improve continuously.
| Metric | GPT-5 mini | GPT-5 nano | GPT-OSS 120B Fireworks | GPT-OSS 20B Fireworks |
|---|---|---|---|---|
| Total Cost | $0.659436 | $0.225682 | $0.542208 | $0.390457 |
| Avg Request Duration | 14.020s | 11.986s | 10.805s | 10.908s |
| Total Tokens | 1,374,832 | 1,654,896 | 3,304,304 | 5,248,592 |
| Total Error | 0 | 0 | 0 | 0 |
| Total Calls | 376 | 392 | 536 | 616 |
| Percentage Passed | 81.12% | 73.72% | 71.27% | 55.84% |
Tweets about Evals
after leading a few projects, i've found that once you've set up the evals + experiment harness and make it easy to tweak config and prompts with 1-click run + eval, teams enjoy running experiments and hill climbing those numbers, and progress comes quickly.
— Eugene Yan (@eugeneyan) August 26, 2025
but setting up that…
I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to judge outputs longer — than they should. This is because building evals is viewed as a massive investment (say, creating 100 or…
— Andrew Ng (@AndrewYNg) April 17, 2025
One interesting outcome of building AI applications/products (those on top of LLMs):
— Gergely Orosz (@GergelyOrosz) May 6, 2025
o11y (observability) becomes SO important! You need to monitor, monitor, monitor; alert, alert alert!!
This is b/c LLMs are nondeterministic: so you can forget automated testing.
Big change
evals are surprisingly often all you need
— Greg Brockman (@gdb) December 9, 2023
"Evals are emerging as the real moat for Al startups." — @garrytan (YC CEO)
— Lenny Rachitsky (@lennysan) April 8, 2025
"Writing evals is going to become a core skill for product managers." — @kevinweil (OpenAI CPO)
"If there is one thing we can teach people, it's that writing evals is probably the most important thing."… pic.twitter.com/iYGM5vHLIA
End‑to‑End LLM App Development Environment
Ensure your AI apps and agents are production‑ready with integrated tools to trace, evaluate, and iterate during development.
Tracing
Visualize and debug the flow of data through your generative applications. Identify bottlenecks in LLM calls, understand agentic paths, and ensure expected behavior.
Datasets & Experiments
Accelerate iteration cycles with native dataset management and experiment runs to compare prompts, tools, and retrieval settings.
Prompt Playground & Management
Test prompt changes and get feedback across datasets. Version, review, and promote prompts with confidence.
Evals: Online & Offline
Assess task performance with fast, extensible eval templates or bring your own evaluation logic. Run offline against datasets or online via shadow traffic.
What you get
- Trace explorer with token/cost visibility
- Experiment tracking with leaderboard views
- Prompt registry with versioning and approvals
- Reusable eval suites with scoring & rubrics
Built for teams
- Role‑based access controls
- Comments, review flows, and audit history
- Templates for common LLM tasks
- APIs and SDKs for CI integration
Production‑grade Observability at Scale
Automatically monitor performance, enforce guardrails, and surface patterns for continuous improvement of AI applications.
Intelligent search helps find and capture data points of interest. Filter, categorize, and save datasets to enable deeper analysis or automated workflows.
Mitigate risk with proactive safeguards over both inputs and outputs, including toxicity, PII, jailbreak detection, and policy enforcement.
Always‑on monitoring with dashboards and alerts for hallucination rates, safety violations, latency, cost, and user experience metrics.
Streamlined workflows to flag misinterpretations, correct errors, and capture human feedback that improves model and agent behavior.
Track spend by model, team, environment, and feature. Optimize prompts, caching, and retrieval to control cost without sacrificing quality.
Tie model and agent performance to outcomes like CSAT, deflection, conversion, or time‑to‑resolution with attribution and cohorting.
Offline Evaluations
Deterministically score prompts, tools, and retrieval strategies over curated datasets.
- Golden sets and human‑labeled rubrics
- Regression detection across versions
- Fast iteration with leaderboard views
Online Evaluations
Validate in production using shadow traffic, A/B tests, and user feedback signals.
- Canary and cohort‑based releases
- Real‑time alerts on quality gates
- Continuous improvement loops