AI teams are shipping faster than ever — but evaluating LLM behavior is still the hardest part.
Future AGI is changing that with an evaluation stack built for real production workloads.
Why evaluation is broken today
Most teams rely on:
- human review,
- scattered prompt tests,
- inconsistent QA,
- and no clear benchmarks for safety or reliability.
This slows releases and hides failure cases until they appear in production.
What Future AGI does differently
Future AGI provides an SDK + platform that evaluates any LLM output instantly:
- 🔐 Safety & guardrails tests (prompt injection, harmful output, PII leakage)
- 🧠 RAG & context evaluations (groundedness, hallucination detection)
- ⚙️ JSON / function-calling validation
- 🎭 Tone, sentiment, and behavior checks
- 📊 Similarity metrics (ROUGE, embeddings, heuristics)
No human labeling. No ground truth required.
Built for real pipelines
Future AGI integrates with:
- LangChain
- Langfuse
- TraceAI
- CI/CD (GitHub Actions)
Teams can benchmark, test, debug, and monitor models with the same toolset.
Why this matters
As LLMs move into agents, automation, and enterprise workflows, trust and predictability become essential.
Future AGI gives teams an evaluation layer that scales as fast as their AI systems.
👉 Full documentation: https://github.com/future-agi/ai-evaluation
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