langfuse VS Prompt-Engineering-Guide

Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.

Congrats on the Show! How’s this different from https://github.com/langfuse/langfuse? The exports seems really interesting

Thus, we started working on Langfuse.com (GitHub) to establish an open source LLM engineering platform with tightly integrated features for tracing, prompt management, and evaluation. In the beginning we just solved our own and our friends’ problems. Today we are at over 1000 projects which rely on Langfuse, and 2.3k stars on GitHub. You can either self-host Langfuse or use the cloud instance maintained by us.

- We want to build a tool that is recommended here on HN: you can build a tool you would want to use yourself.

Please see more details here: https://langfuse.com/careers or reach out directly to me: [email protected]

[1] https://github.com/langfuse/langfuse

[2] https://create.t3.gg/

We struggled with this ourselves while building LLM-based products and then open-sourced our observability/monitoring tool [1]. Many use it to track RAG and agents in production, run custom evals on the production traces (focused on hallucination), and track how metrics are different across releases or customers. Feel free to dm if there is something specific you are looking to solve, happy to help.

[1] https://github.com/langfuse/langfuse

Visit us on Discord and Github to engage with our project.

Maybe try https://github.com/langfuse/langfuse

It was recently shared on HN

Prompt Engineering Guide is the holy grail of all guides, aiming to make it easier to stay up-to-date with prompt engineering guides, techniques, applications, and papers. If you are getting started, this is an excellent place to start.

Guides, papers, lecture, notebooks and resources for prompt engineering: https://github.com/dair-ai/Prompt-Engineering-Guide

this has been a great resource. approachable and great for practitioners. it's frequently updated with new papers and techniques https://www.promptingguide.ai/

The idea behind prompt engineering is to construct the queries given to the language models to optimise their performance. This helps to guide them to generate the desired output by fine-tuning their response. There is a plethora of research papers out there on different forms of prompt engineering. DAIR.AI published a guide on prompt engineering that you might find useful to get started.

Prompt Engineering Guide

Let's try to understand what is Prompt Engineering is all about. Here's the quote from Prompt Engineering Guide. DAIR-AI

I found this resource [0] handy for getting a grasp on all the different terms people use (zero/one-shot, tree of thoughts, RAG, etc). It's not super detailed, but was enough for me (a professional developer) to get started on some side projects with Mistral.

[0] https://www.promptingguide.ai/

There are better guides out there too

- https://www.promptingguide.ai/readings

- https://github.com/dair-ai/Prompt-Engineering-Guide/tree/mai...

- https://github.com/microsoft/promptbase (this one is less of a guide, but is likely the current SoTA)