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 a method for converting PDFs to Markdown using a local LLM (Gemma 3 via Ollama), focusing on privacy and efficiency. It involves rendering PDF pages as images and then using the LLM for content extraction, even from scanned PDFs.
This blog post details findings from the Semantic Telemetry Project, analyzing user engagement with Microsoft Copilot. Key takeaways include the correlation between complex task engagement and continued tool use, the increasing complexity of tasks performed by novice users, and the importance of AI expertise matching user expertise for satisfaction.
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.
This article discusses using entropy and variance of entropy (VarEntropy) to detect hallucinations in LLM function calling, focusing on how structured outputs allow for identifying errors through statistical anomalies in token confidence.
Grammarly has achieved ISO/IEC 42001:2023 certification, demonstrating its commitment to responsible AI development and deployment, focusing on security, transparency, and alignment with human values.
This article details six practical use cases for Model Context Protocol (MCP) to automate workflows using AI agents and integrations with tools like Slack, Google Calendar, BigQuery, Linear, GitHub, and HubSpot. It highlights the impact of these automations on team efficiency and productivity.
Transformer Lab is an open-source application for advanced LLM engineering, allowing users to interact, train, fine-tune, and evaluate large language models on their own computer. It supports various models, hardware, and inference engines and includes features like RAG, dataset building, and a REST API.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
A follow-up article to a previous piece on Gen AI usage, noting a roughly even split between personal and business applications.
