# LLM Evaluators Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/llm-evaluators
> Markdown URL: https://aitinkerers.org/technologies/llm-evaluators.md
> Technology record last updated: 2026-05-01T03:23:51Z
> Generated: 2026-09-20T15:43:47Z

LLM Evaluators (LLMs-as-judges) are automated frameworks that score and critique LLM outputs against key metrics: hallucination, relevance, and safety.

LLM Evaluators are mission-critical tools: they systematically assess LLM application performance, acting as a scalable alternative to costly human review. The core mechanism is the 'LLM-as-a-Judge' approach: a second, prompt-engineered LLM is used to grade the first model's output, providing binary (pass/fail) or score-based results (Source 1.1, 1.10). Frameworks like DeepEval offer 50+ research-backed metrics, including Hallucination, Answer Relevancy, and Contextual Precision (Source 2.3, 1.6). This capability is essential for managing the non-deterministic nature of LLMs, ensuring production-grade reliability, and catching failures like factual errors or compliance issues before they impact end-users (Source 1.4, 1.1).

- Official technology site: https://deepeval.com
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [LABEL: LLM Assistance for Better Evaluation Labels](https://seattle.aitinkerers.org/talks/rsvp_kadYvSmadkQ)

A demo workflow and UX for labeling data, using it to evaluate LLM-evaluators, and then aligning the LLM-evaluator to human judgments (and perhaps optimizing the evaluator!) P.S., Kyle Corbitt of OpenPipe will be demoing something similar, and I hope to have a faceoff with them by going before them so the audience can decide the pros and cons of each.

- Event context: AI Tinkerers - Seattle - October 2024 Meetup — 2024-10-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_kadYvSmadkQ

## Related Technologies

- [BERT](https://aitinkerers.org/technologies/bert) ([Markdown](https://aitinkerers.org/technologies/bert.md)) — 179 public demos
- [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos
- [Data labeling](https://aitinkerers.org/technologies/data-labeling) ([Markdown](https://aitinkerers.org/technologies/data-labeling.md)) — 1 public demo
- [GPT-3](https://aitinkerers.org/technologies/gpt-3) ([Markdown](https://aitinkerers.org/technologies/gpt-3.md)) — 191 public demos
- [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos
- [Human judgments](https://aitinkerers.org/technologies/human-judgments) ([Markdown](https://aitinkerers.org/technologies/human-judgments.md)) — 1 public demo
- [Llama-2](https://aitinkerers.org/technologies/llama-2) ([Markdown](https://aitinkerers.org/technologies/llama-2.md)) — 227 public demos
- [OpenPipe](https://aitinkerers.org/technologies/openpipe) ([Markdown](https://aitinkerers.org/technologies/openpipe.md)) — 4 public demos
- [PaLM 2](https://aitinkerers.org/technologies/palm-2) ([Markdown](https://aitinkerers.org/technologies/palm-2.md)) — 116 public demos
- [RoBERTa](https://aitinkerers.org/technologies/roberta) ([Markdown](https://aitinkerers.org/technologies/roberta.md)) — 118 public demos