A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

• ReWOO

  3 851 7.0 Python

  Decoupling Reasoning from Observations for Efficient Augmented Language Models

It's maybe interesting to think about a simple chat history over time giving some of the knowledge needed for improved interactions as related to the more complex patterns outlined here.

Here's a similar paper I ran across this morning: https://arxiv.org/abs/2305.18323. Github is here: https://github.com/billxbf/ReWOO

I indexed both documents with my own project, which uses semantic graphs to help with prompt assembly: https://github.com/FeatureBaseDB/DoctorGPT. DoctorGPT doesn't have dynamic prompt chaining yet, but I'm working on it. I hesitate posting any of the analysis of these papers using DoctorGPT here because it would be generated by the LLM, and not me...and some people seem to have an issue with that given this is a human forum.

My sense is that SKGs are important in refining questions, offering alternative approaches, managing context, reflecting on LLM responses, and more.

• DoctorGPT

  7 213 7.2 Python

  💻📚💡 DoctorGPT provides advanced LLM prompting for PDFs and webpages. (by FeatureBaseDB)

  

It's maybe interesting to think about a simple chat history over time giving some of the knowledge needed for improved interactions as related to the more complex patterns outlined here.

Here's a similar paper I ran across this morning: https://arxiv.org/abs/2305.18323. Github is here: https://github.com/billxbf/ReWOO

I indexed both documents with my own project, which uses semantic graphs to help with prompt assembly: https://github.com/FeatureBaseDB/DoctorGPT. DoctorGPT doesn't have dynamic prompt chaining yet, but I'm working on it. I hesitate posting any of the analysis of these papers using DoctorGPT here because it would be generated by the LLM, and not me...and some people seem to have an issue with that given this is a human forum.

My sense is that SKGs are important in refining questions, offering alternative approaches, managing context, reflecting on LLM responses, and more.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• prompt-engineering

  18 7,932 5.1

  Tips and tricks for working with Large Language Models like OpenAI's GPT-4.

  

I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.

These are good straight-to-the-point guides:

- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering

- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...

- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...

- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...

tl;dr:

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