#014 Building Predictable Agents through Prompting, Compression, and Memory Strategies

How AI Is Built

תוכן מסופק על ידי Nicolay Gerold. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Nicolay Gerold או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

1+ y ago 32:13

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In this conversation, Nicolay and Richmond Alake discuss various topics related to building AI agents and using MongoDB in the AI space. They cover the use of agents and multi-agents, the challenges of controlling agent behavior, and the importance of prompt compression.

When you are building agents. Build them iteratively. Start with simple LLM calls before moving to multi-agent systems.

Main Takeaways:

Prompt Compression: Using techniques like prompt compression can significantly reduce the cost of running LLM-based applications by reducing the number of tokens sent to the model. This becomes crucial when scaling to production.
Memory Management: Effective memory management is key for building reliable agents. Consider different memory components like long-term memory (knowledge base), short-term memory (conversation history), semantic cache, and operational data (system logs). Store each in separate collections for easy access and reference.
Performance Optimization: Optimize performance across multiple dimensions - output quality (by tuning context and knowledge base), latency (using semantic caching), and scalability (using auto-scaling databases like MongoDB).
Prompting Techniques: Leverage prompting techniques like ReAct (observe, plan, act) and structured prompts (JSON, pseudo-code) to improve agent predictability and output quality.
Experimentation: Continuous experimentation is crucial in this rapidly evolving field. Try different frameworks (LangChain, Crew AI, Haystack), models (Claude, Anthropic, open-source), and techniques to find the best fit for your use case.

Richmond Alake:

Nicolay Gerold:

00:00 Reducing the Scope of AI Agents

01:55 Seamless Data Ingestion

03:20 Challenges and Considerations in Implementing Multi-Agents

06:05 Memory Modeling for Robust Agents with MongoDB

15:05 Performance Optimization in AI Agents

18:19 RAG Setup

AI agents, multi-agents, prompt compression, MongoDB, data storage, data ingestion, performance optimization, tooling, generative AI

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#Tech #Nicolay Gerold #Technology #LLM #Machine Learning #Data Engineering