prompt-engineering
Arthas
| prompt-engineering | Arthas | |
|---|---|---|
| 18 | 4 | |
| 7,932 | 34,693 | |
| 1.7% | 0.6% | |
| 5.1 | 8.3 | |
| 6 months ago | 7 days ago | |
| Java | ||
| MIT License | Apache License 2.0 |
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prompt-engineering
- Ask HN: Any good collection of writing prompts for GPT 3.5/4?
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Show HN: LLM Agent Paper List
An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).
You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react
- FLaNK Stack Weekly for 20 June 2023
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What are your long-term career goals?
Well, if developers get replaced by AI, then who are the managers going to manage :). I personally don't think AI is just going to replace us. The way we work will continue to change as new AI tools come out. I'm taking time to tinker with new tools and seeing how others do as well (e.g., I found Brex's tips and tricks for working with LLMs very insightful: https://github.com/brexhq/prompt-engineering).
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A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
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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(2/2) May 2023
Brex's Prompt Engineering Guide (https://github.com/brexhq/prompt-engineering)
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
- Brex’s Prompt Engineering Guide
Arthas
- FLaNK Stack Weekly for 20 June 2023
-
What's the most interesting open-source project to study?
Arthas
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Best performance monitoring tools?
Arthas can be pretty useful depending on what you're looking for. https://github.com/alibaba/arthas
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Alibaba Summer of Code 2020 – Arthas Final Report
Issue
What are some alternatives?
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
bucket4j - Java rate limiting library based on token-bucket algorithm.
Prompt-Engineering-Guide - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
async-profiler - Sampling CPU and HEAP profiler for Java featuring AsyncGetCallTrace + perf_events [Moved to: https://github.com/async-profiler/async-profiler]
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
java-diff-utils - Diff Utils library is an OpenSource library for performing the comparison / diff operations between texts or some kind of data: computing diffs, applying patches, generating unified diffs or parsing them, generating diff output for easy future displaying (like side-by-side view) and so on.
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
async-profiler - Sampling CPU and HEAP profiler for Java featuring AsyncGetCallTrace + perf_events
chathub - All-in-one chatbot client
opentelemetry-java - OpenTelemetry Java SDK
canal - 阿里巴巴 MySQL binlog 增量订阅&消费组件
Flowable (V6) - A compact and highly efficient workflow and Business Process Management (BPM) platform for developers, system admins and business users.