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Prompt engineering is dead, long live context engineering

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Manage episode 498374791 series 3541344
Content provided by Enrollify, Ardis Kadiu, and Dr. JC Bonilla. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Enrollify, Ardis Kadiu, and Dr. JC Bonilla or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

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

- - - -
Connect With Our Co-Hosts:
Ardis Kadiu
https://www.linkedin.com/in/ardis/
https://twitter.com/ardis

Dr. JC Bonilla
https://www.linkedin.com/in/jcbonilla/
https://twitter.com/jbonillx

About The Enrollify Podcast Network:
Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!

Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

  continue reading

99 episodes

Artwork
iconShare
 
Manage episode 498374791 series 3541344
Content provided by Enrollify, Ardis Kadiu, and Dr. JC Bonilla. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Enrollify, Ardis Kadiu, and Dr. JC Bonilla or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

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

- - - -
Connect With Our Co-Hosts:
Ardis Kadiu
https://www.linkedin.com/in/ardis/
https://twitter.com/ardis

Dr. JC Bonilla
https://www.linkedin.com/in/jcbonilla/
https://twitter.com/jbonillx

About The Enrollify Podcast Network:
Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!

Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

  continue reading

99 episodes

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