This article details new prompting techniques for ChatGPT-4.1, emphasizing structured prompts, precise delimiting, agent creation, long context handling, and chain-of-thought prompting to achieve better results.
This article explains prompt engineering techniques for large language models (LLMs), covering methods like zero-shot, few-shot, system, contextual, role, step-back, chain-of-thought, self-consistency, ReAct, Automatic Prompt Engineering and code prompting. It also details best practices and output configuration for optimal results.
This article details an iterative process of using ChatGPT to explore the parallels between Marvin Minsky's "Society of Mind" and Anthropic's research on Large Language Models, specifically Claude Haiku. The user experimented with different prompts to refine the AI's output, navigating issues like model confusion (GPT-2 vs. Claude) and overly conversational tone. Ultimately, prompting the AI with direct source materials (Minsky’s books and Anthropic's paper) yielded the most insightful analysis, highlighting potential connections like the concept of "A and B brains" within both frameworks.
A guide on implementing prompt engineering patterns to make RAG implementations more effective and efficient, covering patterns like Direct Retrieval, Chain of Thought, Context Enrichment, Instruction-Tuning, and more.
Does Good Software Engineering Improve the Performance of Large Language Models with Software Tasks?The article discusses how structured, modular software engineering practices enhance the effectiveness of large language models (LLMs) in software development tasks. It emphasizes the importance of clear and coherent code, which allows LLMs to better understand, extend functionality, and debug. The author shares experiences from the Bad Science Fiction project, illustrating how well-engineered code improves AI collaboration.
Key takeaways:
1. **Modular Code**: Use small, well-documented code blocks to aid LLM performance.
2. **Effective Prompts**: Design clear, structured prompts by defining context and refining iteratively.
3. **Chain-of-Thought Models**: Provide precise inputs to leverage structured problem-solving abilities.
4. **Prompt Literacy**: Master expressing computational intent clearly in natural language.
5. **Iterative Refinement**: Utilize AI consultants for continuous code improvement.
6. **Separation of Concerns**: Organize code into server and client roles for better AI interaction.
Key takeaways:
1. **Modular Code**: Use small, well-documented code blocks to aid LLM performance.
2. **Effective Prompts**: Design clear, structured prompts by defining context and refining iteratively.
3. **Chain-of-Thought Models**: Provide precise inputs to leverage structured problem-solving abilities.
4. **Prompt Literacy**: Master expressing computational intent clearly in natural language.
5. **Iterative Refinement**: Utilize AI consultants for continuous code improvement.
6. **Separation of Concerns**: Organize code into server and client roles for better AI interaction.
The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.
The article discusses the rise of prompt engineering as a discipline for tuning prompts to interact with large language models (LLMs) effectively. It addresses the challenges of curating and maintaining a high-quality prompt store, highlighting the difficulties due to overlapping prompts. It uses content writing as an example to illustrate the need for a systematic approach to retrieving optimal prompts.
Zero Human Code: What I Learned from Forcing AI to Build (and Fix) Its Own Code for 27 Straight DaysAn experiment in agentic AI development, where AI tools were tasked with building and maintaining a full-service product, ObjectiveScope, without direct human code modifications. The process highlighted the challenges and constraints of AI-driven development, such as deteriorating context management, technical limitations, and the need for precise prompt engineering.
Jeff Dean discusses the potential of merging Google Search with large language models (LLMs) using in-context learning, emphasizing enhanced information processing and contextual accuracy while addressing computational challenges.
Guidelines for using large language models to improve Python code quality in casual usage.
