9 Methods and Techniques You Must Know (AI 101 Guide Recap)

By Sayed Allam

Date: July 05, 2025

Introduction:

This article highlights 9 essential AI techniques that emerged in the first half of 2025. These techniques are pivotal in shaping the future of AI technology, and understanding them will place you at the forefront of AI advancements.

1. What is HtmlRAG, Multimodal RAG, and Agentic RAG?

Main Concept: Enhancements to traditional Retrieval-Augmented Generation (RAG) methods.

• HtmlRAG: Directly works with the HTML version of text, enabling more efficient processing of web-based data.
• Multimodal RAG: This method can retrieve information from not only text but also images, creating a richer, multi-modal context for better decision-making.
• Agentic RAG: Incorporates autonomous capabilities within the RAG framework, allowing AI systems to perform tasks independently based on retrieved data.

Why it Matters: These methods overcome the limitations of the original RAG approach and align with the evolving trends in 2025, enabling better performance in complex scenarios.

2. Everything You Need to Know About Knowledge Distillation

Core Technique: Transferring knowledge from a larger, more powerful model (the teacher) to a smaller, more efficient model (the student).

• Importance: Knowledge distillation allows smaller models to inherit the capabilities of larger models, reducing computational cost and enabling them to perform at high levels despite being resource-constrained.
• Key Concepts: There are several types of knowledge distillation, scaling laws that govern how smaller models perform as they inherit knowledge, and practical use cases that demonstrate its efficacy in the real world.

Why it Matters: Knowledge distillation continues to be one of the most powerful tools in training smaller, more efficient AI models without compromising performance.

3. The Keys to Prompt Optimization

Practical Strategy: Making the most of large language models (LLMs) by crafting better prompts.

Four Main Pillars of Prompt Optimization:

• Expansion: Adding more context to your prompts to help the AI understand the broader picture.