This Is What Actually Works in 2026!

Introduction to Advanced Prompt Engineering

Overview of the Course

• In 2023, a prompt engineering course was published on YouTube, gaining over 1 million views and 40,000 likes.

• The speaker notes significant changes in the field by 2026 and introduces five advanced tactics that can enhance results in AI prompting.

Importance of Effective Prompting

• A brief refresher on prompt engineering emphasizes that better prompts yield better results.

• Four pillars of effective prompts are identified: role, task, context, and rules. Most users neglect one or more pillars, leading to suboptimal outcomes.

Advanced Tactic #1: Recursive Brainstorming

Concept Explanation

• Recursive brainstorming involves generating ideas in layers; starting with broad concepts and drilling down into specifics.

Practical Example

• An initial prompt asks for the top five business ideas for the AI era. The first idea is then expanded into five sub-ideas.

• This process can be repeated recursively to explore deeper levels of ideas without manual effort.

Automation Tools

• Manual recursive brainstorming can be tedious; thus, automation through coding is suggested.

• A free playground tool is introduced where users can visualize this brainstorming technique easily by selecting parameters like max depth and ideas per level.

Visualization and Results

Using the Playground Tool

• Users input their prompts into the tool which generates a brainstorm tree displaying various levels of ideas.

Output Features

• The tool provides detailed sections for each idea along with scores indicating their potential value.

• Users can export results in JSON format for further use or integration into other applications.

Advanced Tactic #2: Web JSON Technique

Introduction to Web JSON Technique

• This tactic focuses on prompting AI to search online resources and return data in structured JSON format.

Use Case Demonstration

• An example shows how copying JSON data into a carousel generator tool creates visual content quickly from structured data.

Broader Applications

• The web JSON technique has multiple applications including building dashboards with live data and creating comparison tables among products. Understanding this method opens up numerous possibilities for efficient data handling.

Building AI Products and Feedback Loops

Introduction to AI Product Development

• The speaker introduces the concept of building products with AI, mentioning an open-source car generator that will be shared in future videos. Encouragement to subscribe for updates is provided.

• A demonstration of a web JSON technique is mentioned, where users can select providers and models to construct desired outputs or paste Python code.

Tactic Number Three: The Feedback Loop

• The feedback loop is introduced as a method for improving initial responses from language models through iterative feedback.

• An example prompt asks for a simple explanation of quantum computing suitable for an 8-year-old, illustrating how the model generates an initial response.

• The process involves critiquing the generated answer by asking the model to analyze its complexity and suggest simpler wording.

• Multiple iterations are possible, allowing continuous refinement until optimal clarity is achieved; however, over-optimization may occur if too many iterations are performed.

Playground Demonstration

• In the playground tool, users can input prompts and evaluate initial answers based on selected criteria like clarity and creativity.

• Users can choose between single or dual provider modes for generating critiques within the feedback loop.

Tactic Number Four: The LM Judge

• Transitioning from self-improvement to evaluation by introducing a judge model that assesses multiple responses from different models.

• Three judging modes are described: selecting the best answer, synthesizing elements into one improved response, and comparing strengths/weaknesses of each model's output.

Practical Application of LM Judge

• Users can input results from various models into the judge system (e.g., OpenAI GPT4), which evaluates them based on predefined criteria.

• Results include scores and detailed breakdown analyses that highlight strengths and weaknesses across different responses.

Importance of Evaluation in AI Responses

• Emphasizes using this multi-model approach especially in NLP tasks requiring intensive reasoning; it helps identify areas for improvement in generated content.

Tactic Number Five: LM Retrieval

• Introduces retrieval techniques using structured documentation available within libraries to enhance understanding and application of language models.

Semantic Routing and AI Navigation

Understanding Semantic Routing

• The speaker introduces the concept of semantic routing, which allows AI to navigate a structured content tree to retrieve accurate data based on user queries.

• An example application, "onto digest," is mentioned, showcasing how it summarizes video content and provides interactive features like quizzes and flashcards.

• Instead of processing full transcripts, the system transforms video content into a structured format for efficient navigation by the language model.

Practical Applications of Semantic Routing

• When asked to summarize specific sections (e.g., a 12-month plan), the AI uses semantic routing to pinpoint relevant chunks rather than retrieving entire transcripts.

• This method conserves tokens and enhances accuracy in responses, particularly beneficial for chatbots.

Prompting Tips for Effective AI Interaction

Critique as a Starting Point

• The first tip emphasizes starting with criticism when brainstorming ideas. This approach encourages honest feedback from AI rather than uncritical support.

Creative Mixing for Unique Ideas

• The second tip suggests combining unrelated topics to generate unique product ideas. For instance, merging Netflix's business model with language learning challenges can yield innovative concepts.

Importance of Context in Prompts

• The third tip highlights that context is crucial when coding with AI. Providing documentation or code snippets helps guide the AI in producing relevant outputs.

Learning Through Building Projects

• Instead of theoretical explanations, users are encouraged to learn by building projects with AI assistance. This hands-on approach fosters deeper understanding through practical application.

Using the Playground Application

Accessing and Utilizing the Playground

• Instructions are provided on accessing a free application that allows users to experiment with various models without installation.

Features of the Playground Tool

• Users can input API keys for different providers and interact with local models within the playground environment. It also includes tools for real-time model comparison during chats.