Add ZenML LLMops resource boris shared (openai#1589)

Author: ibigio

articles/related_resources.md

@@ -15,6 +15,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts.
 - [LiteLLM](https://github.com/BerriAI/litellm): A minimal Python library for calling LLM APIs with a consistent format.
 - [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data.
+- [LLMOps Database](https://www.reddit.com/r/LocalLLaMA/comments/1h4u7au/a_nobs_database_of_how_companies_actually_deploy/): Database of how companies actually deploy LLMs in production.
 - [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
 - [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
 - [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation.
@@ -25,7 +26,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts.
 - [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
 - [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
-- [Vellum](https://www.vellum.ai/): A paid AI product development platform to experiment with, evaluate, and deploy advanced LLM apps. 
+- [Vellum](https://www.vellum.ai/): A paid AI product development platform to experiment with, evaluate, and deploy advanced LLM apps.
 - [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
 - [YiVal](https://github.com/YiVal/YiVal): An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies.
 
@@ -36,7 +37,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Lil'Log Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/): An OpenAI researcher's review of the prompt engineering literature (as of March 2023).
 - [OpenAI Cookbook: Techniques to improve reliability](https://cookbook.openai.com/articles/techniques_to_improve_reliability): A slightly dated (Sep 2022) review of techniques for prompting language models.
 - [promptingguide.ai](https://www.promptingguide.ai/): A prompt engineering guide that demonstrates many techniques.
-- [Xavi Amatriain's Prompt Engineering 101 Introduction to Prompt Engineering](https://amatriain.net/blog/PromptEngineering) and [202 Advanced Prompt Engineering](https://amatriain.net/blog/prompt201): A basic but opinionated introduction to prompt engineering and a follow up collection with many advanced methods starting with CoT.   
+- [Xavi Amatriain's Prompt Engineering 101 Introduction to Prompt Engineering](https://amatriain.net/blog/PromptEngineering) and [202 Advanced Prompt Engineering](https://amatriain.net/blog/prompt201): A basic but opinionated introduction to prompt engineering and a follow up collection with many advanced methods starting with CoT.
 
 ## Video courses
 
@@ -46,7 +47,6 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Scrimba course about Assistants API](https://scrimba.com/learn/openaiassistants): A 30-minute interactive course about the Assistants API.
 - [LinkedIn course: Introduction to Prompt Engineering: How to talk to the AIs](https://www.linkedin.com/learning/prompt-engineering-how-to-talk-to-the-ais/talking-to-the-ais?u=0): Short video introduction to prompt engineering
 
-
 ## 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) rises from 18% to 57%.