Generation AI

Prompt engineering is dead, long live context engineering

Episode Summary

In this technical deep dive, Generation AI explores the evolution from prompt engineering to context engineering - a critical shift in how we build intelligent AI systems. Hosts Ardis Kadiu and Petar Djordjevic from Element451 break down why static prompts are no longer enough and how dynamic context management is the key to creating truly smart agents. They explain the technical architecture behind retrieval augmented generation (RAG), discuss the challenges of building multi-agent systems that coordinate effectively, and reveal how Element451's new bulk jobs feature represents the cutting edge of context engineering in higher education. The episode concludes with an analysis of Mark Zuckerberg's vision for "personal superintelligence" - always-on AI assistants that remember everything about you. This matters because institutions need to understand that the success of AI agents depends entirely on having rich, well-structured data and proper context management - not just smart models.

Episode Notes

Introduction and the Shift from Prompt to Context Engineering (00:00:00)

Welcome back Petar Djordjevic as co-host for the third time
The transformation from static prompt libraries to dynamic context systems
Why GPT-4's evolution to reasoning models changed everything
How agents use tools to gather real-time information instead of relying on frozen knowledge

Defining Context Engineering vs Prompt Engineering (00:04:11)

Context engineering as managing dynamic information for AI tasks
The evolution from one-shot prompt problems to complex agent workflows
How automation requirements drove the need for context engineering
Why "it's their first day on the job every day" for AI models

Deep Dive into RAG (Retrieval Augmented Generation) (00:17:17)

The complete RAG pipeline: from user query to accurate response
Breaking down queries into multiple intents for better results
Vector databases and metadata attachment for information storage
The importance of combining keyword search with semantic search

Advanced RAG Techniques and Challenges (00:21:08)

Data preparation: parsing PDFs and extracting meaningful chunks
Why semantic search alone isn't enough - the CS101 problem
Re-ranking and post-processing to get the most relevant results
How to handle citations and build user trust in AI responses

Building Complex Agent Systems at Element451 (00:33:08)

Element451's new Bulk Jobs feature as a case study
The research phase: gathering student data, interaction history, and context
Why data-rich platforms are essential for successful agents
Moving from segment-based personalization to true "segment of one"

Context Pruning and Tool Selection (00:41:31)

Why you can't just throw all data into the context window
Performance degradation with large contexts - the needle in haystack problem
Selecting the right tools for each task (SMS vs WhatsApp example)
How to compress and adapt content for optimal performance

Multi-Agent Coordination and State Management (00:46:48)

The challenge of multiple agents working on the same student
Context writing: how agents remember what they did and why
Preventing redundant actions across different departments
Building systems that coordinate like experienced teams

Common Mistakes in Context Engineering (00:50:17)

The danger of being "lazy about context" and assuming AI is smart enough
Why domain expertise is crucial for building effective agents
The importance of vertical-specific agents (Cursor, Harvey, Sierra examples)
How Element451 leverages its CRM data for education-specific agents

The Future: Personal Superintelligence (00:53:18)

Mark Zuckerberg's vision of always-on, memory-rich personal AI
Meta's glasses as the computing platform of the future
Andrej Karpathy's small model with massive context approach
Challenges: ambient monitoring, recall/summary, lifelong memory files