PROMPT ENGINEERING

Constructing Research Agents for Tech Insights

ChatGPT something like: “Please scout all of tech for me and summarize trends and patterns based on what you think that I could be serious about,” you recognize that you simply’d get something...

Declarative and Imperative Prompt Engineering for Generative AI

refers back to the careful design and optimization of inputs (e.g., queries or instructions) for guiding the behavior and responses of generative AI models. Prompts are typically structured using either the declarative or...

How To Significantly Enhance LLMs by Leveraging Context Engineering

is the science of providing LLMs with the proper context to maximise performance. Once you work with LLMs, you sometimes create a system prompt, asking the LLM to perform a certain task. Nonetheless,...

Apprehensive About AI? Use It to Your Advantage

Never miss a brand new edition of , our weekly newsletter featuring a top-notch choice of editors’ picks, deep dives, community news, and more. The controversy around AI’s impact on tech careers has been polarizing—to place it...

Meta, ‘Rama’ Prompt Optimization Developer Tools Disclosure

Meta has introduced a developer tool that robotically optimizes the prompt for 'LLAMA'. The tool was launched to support the usage of Rama more easily by reducing other prompt cars for every artificial intelligence...

AI Agents from Zero to Hero — Part 2

In Part 1 of this tutorial series, we introduced AI Agents, autonomous programs that perform tasks, make decisions, and communicate with others.  Agents perform actions through Tools. It would occur that a Tool doesn’t work...

Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs 

Creating efficient prompts for big language models often starts as a sure bet… but it surely doesn’t at all times stay that way. Initially, following basic best practices seems sufficient: adopt the persona of...

Enhancing RAG: Beyond Vanilla Approaches

Retrieval-Augmented Generation (RAG) is a robust technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This text explores...

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