Prompt engineering // update review
LLMs have limited reasoning abilities; that’s why prompt engineering and programming comes to the rescue. LLMs are nothing else as pre-trained generators based on semantically closed examples. If there is no close example, you are in trouble, there is no way to generate appropriate output. But you can put such an example in the prompt. A good prompt does the trick. Do we need LLMs then?)
Complex reasoning tasks can’t be solved just by prompt engineering, advanced methods are required.
Prompt design and engineering has become an important discipline in just the past few months. In this paper, we provide an introduction to the main concepts and design approaches. We also provide more advanced techniques all the way to those needed to design LLM-based agents. We finish by providing a list of existing tools for prompt engineering.
Here is a summary of the key points from the research paper:
The paper provides an introduction to prompt design and engineering for large language models (LLMs). It covers basic prompt concepts and examples, as well as more advanced techniques.
- A prompt is the textual input that guides an LLM’s output. Prompts can include instructions, questions, input data, and examples.
- Basic prompts involve simple questions or instructions. More advanced prompts use techniques like chain of thought prompting to guide logical reasoning.
- Advanced prompt engineering techniques include chain of thought, tree of thought, tools/connectors/skills, automatic reasoning and tool use, self-consistency, reflection, expert prompting, and rails.
- Tools like Langchain, Semantic Kernel, and Guidance support implementation of prompt engineering methods.
- Retrieval augmented generation (RAG) enhances LLMs by retrieving external knowledge. RAG techniques like FLARE iteratively combine prediction and retrieval.
- LLM agents utilize specialized prompting to enable autonomous task completion. Methods like ReWOO, ReAct, and DERA aim to improve agent reasoning and dialog capabilities.
- Prompt engineering is a rapidly evolving field that is becoming crucial for effectively using large language models.
Here is a summary of the key points from the blog post:
- Prompt engineering is the process of designing and refining prompts to guide language model outputs. It is crucial for harnessing the potential of large language models (LLMs) like GPT-3 and DALL-E while mitigating their limitations.
- The paper “Prompt Design and Engineering” by Xavier Amatriain explores techniques like Chain of Thought, Tree of Thought, Tools Connectors, Automatic Multi-step Reasoning and Tool-use, Self-Consistency, Reflection, Expert Prompting, Chains and Rails, Automatic Prompt Engineering, and Retrieval Augmented Generation.
- The post demonstrates implementing these techniques using Haystack 2.0 and LLM providers like OpenAI. It provides code examples and practical applications for each technique, like building prompts for GPT-3 using Python and OpenAPI.
- Techniques are shown to guide LLMs to generate more accurate, relevant and nuanced responses aligned with user needs. This includes incorporating external tools and knowledge, multi-step reasoning, self-consistency checks, reflection, and domain expertise.
- The post offers a comprehensive look at prompt engineering, with the aim of providing developers a valuable resource for applying these techniques in their AI projects. It showcases practical implementation to overcome LLM limitations.
