klotz: prompt engineering* + llm*

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  1. 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.
  2. 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.

    2025-02-25 Tags: , , , , , by klotz
  3. 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.

    2025-02-23 Tags: , , by klotz
  4. An 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.

    2025-02-21 Tags: , , , by klotz
  5. 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.

  6. Guidelines for using large language models to improve Python code quality in casual usage.

  7. This paper presents a detailed vocabulary of 33 terms and a taxonomy of 58 LLM prompting techniques, along with guidelines for prompt engineering and a meta-analysis of natural language prefix-prompting, serving as the most comprehensive survey on prompt engineering to date.

  8. A guide on how to understand and read bank statements effectively, highlighting key components and terms, and discussing the importance for financial management and fraud prevention.

  9. The article discusses the challenges people face when using AI, such as treating it like a search engine, and suggests methods to effectively prompt AI, including treating it like a coworker, providing context, and leveraging its infinite patience for task and thought prompting.

    2024-11-24 Tags: , by klotz
  10. This post discusses the importance of understanding AI model prompts as akin to traditional software programs, highlighting opportunities and challenges for the programming language and software engineering communities in addressing this perspective.

    2024-10-26 Tags: , , by klotz

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