Prompt Engineering Guide | Prompt Engineering Guide #314
Labels
AI-Agents
Autonomous AI agents using LLMs
embeddings
vector embeddings and related tools
finetuning
Tools for finetuning of LLMs e.g. SFT or RLHF
llm
Large Language Models
llm-evaluation
Evaluating Large Language Models performance and behavior through human-written evaluation sets
llm-experiments
experiments with large language models
llm-inference-engines
Software to run inference on large language models
Models
LLM and ML model repos and links
prompt-engineering
Developing and optimizing prompts to efficiently use language models for various applications and re
RAG
Retrieval Augmented Generation for LLMs
Prompt Engineering Guide
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).
Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.
Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It's an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools.
Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, advanced prompting techniques, learning guides, model-specific prompting guides, lectures, references, new LLM capabilities, and tools related to prompt engineering.
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