Merge pull request openai#451 from openai/ted/add-evals-link

adds OpenAI Evals link

Author: ted-at-openai

README.md

@@ -82,6 +82,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
 - [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts.
 - [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
+- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
 
 ### Prompting guides
 
@@ -97,7 +98,6 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Andrej Karpathy's Let's build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY): A detailed dive into the machine learning underlying GPT.
 - [Prompt Engineering by DAIR.AI](https://www.youtube.com/watch?v=dOxUroR57xs): A one-hour video on various prompt engineering techniques.
 
-
 ### Papers on advanced prompting to improve reasoning
 
 - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) go from 18% to 57%.
@@ -111,7 +111,6 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance.
 - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches.
 
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 ## Contributing
 
 If there are examples or guides you'd like to see, feel free to suggest them on the [issues page]. We are also happy to accept high quality pull requests, as long as they fit the scope of the repo.