This post presents technique to improve the your output when prompting ChatGPT.

How to talk to ChatGPT effectively

User intent classification is a crucial aspect of conversational AI, start with machine learning models, but now advanced language models (LLMs) are being explored for this task. Unlike the old methods which is need to labeled datasets exhaustively, LLMs can understand what users mean without all that preparation. This memo explores the application of LLMs in intent classification, highlighting their potential to streamline the process and overcome traditional NLU limitations.

Intent classification by LLM

A comprehensive guide outlining the journey for engineers developing applications on top of Large Language Models (LLMs), covering key stages from understanding AI basics to fine-tuning models.

Story map for LLMs

Latest study and application of Prompt engineering

§ Prompt Engineering

An overview of adversarial prompting in prompt engineering, focusing on understanding risks associated with Large Language Models (LLMs) and strategies for mitigating them.

Adversarial prompting in prompt engineering

Journey of Thought Prompting is an innovative technique that leverages AI to craft more effective prompts for large language models. This approach uses the analytical capabilities of AI assistants to help users create detailed system prompts, fill in missing details, and iteratively refine their prompt engineering skills. It represents a shift towards collaborative AI interaction, enhancing problem-solving capabilities for businesses and engineers.

Journey of thought prompting: Harnessing AI to craft better prompts

An overview of self-refinement, a technique where Large Language Models (LLMs) evaluate and improve their own output without the need for supervised data or reinforcement learning.

LLM's accuracy - self refinement

A comprehensive prompt template to get tailored metrics recommendations for any system. Use this with ChatGPT, Claude, or other LLMs to design monitoring that actually matters.

LLM prompt for metrics planning

A detailed look into the mcp-playbook's internal code flow, data handling for chat logs and prompts, and interactions with GitHub repositories like prompt-db and prompt-log. This technical deep-dive helps developers understand the implementation details for debugging and extending functionality.

MCP playbook code flow

Exploring the motivations behind the MCP Playbook, its role in establishing a dynamic runbook, and new patterns for capturing AI-generated knowledge like chat logs and prompts.

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