DeepLearning.AI

Technical Insights

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

Much has been said about many companies’ desire for more compute (as well as data) to train larger foundation models.

Technical Insights

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

Much has been said about many companies’ desire for more compute (as well as data) to train larger foundation models.

Technical Insights

Agentic Design Patterns Part 5, Multi-Agent Collaboration: Prompting an LLM to play different roles for different parts of a complex task summons a team of AI agents that can do the job more effectively.

Multi-agent collaboration is the last of the four key AI agentic design patterns that I’ve described in recent letters.

Technical Insights

Agentic Design Patterns Part 5, Multi-Agent Collaboration: Prompting an LLM to play different roles for different parts of a complex task summons a team of AI agents that can do the job more effectively.

Multi-agent collaboration is the last of the four key AI agentic design patterns that I’ve described in recent letters.

Technical Insights

Agentic Design Patterns Part 4, Planning: Large language models can drive powerful agents to execute complex tasks if you ask them to plan the steps before they act.

Planning is a key agentic AI design pattern in which we use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a larger task.

Technical Insights

Agentic Design Patterns Part 4, Planning: Large language models can drive powerful agents to execute complex tasks if you ask them to plan the steps before they act.

Planning is a key agentic AI design pattern in which we use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a larger task.

Technical Insights

Agentic Design Patterns Part 3, Tool Use: How large language models can act as agents by taking advantage of external tools for search, code execution, productivity, ad infinitum

Tool use, in which an LLM is given functions it can request to call for gathering information, taking action, or manipulating data, is a key design pattern of AI agentic workflows.

Technical Insights

Agentic Design Patterns Part 3, Tool Use: How large language models can act as agents by taking advantage of external tools for search, code execution, productivity, ad infinitum

Tool use, in which an LLM is given functions it can request to call for gathering information, taking action, or manipulating data, is a key design pattern of AI agentic workflows.

Technical Insights

Agentic Design Patterns Part 2, Reflection: Large language models can become more effective agents by reflecting on their own behavior.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress this year: Reflection, Tool use, Planning and Multi-agent collaboration.

Technical Insights

Agentic Design Patterns Part 2, Reflection: Large language models can become more effective agents by reflecting on their own behavior.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress this year: Reflection, Tool use, Planning and Multi-agent collaboration.

Technical Insights

Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance

I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it.

Technical Insights

Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance

I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it.

Technical Insights

Life in Low Data Gravity: With generative AI, data is bound less tightly to the cloud provider where it’s stored. This has big implications for developers, CIOs, and cloud platforms.

I’ve noticed a trend in how generative AI applications are built that might affect both big companies and developers: The gravity of data is decreasing.

Technical Insights

Life in Low Data Gravity: With generative AI, data is bound less tightly to the cloud provider where it’s stored. This has big implications for developers, CIOs, and cloud platforms.

I’ve noticed a trend in how generative AI applications are built that might affect both big companies and developers: The gravity of data is decreasing.

Technical Insights

The Dawning Age of Agents: LLM-based agents that act autonomously are making rapid progress. Here's what we have to look forward to.

Progress on LLM-based agents that can autonomously plan out and execute sequences of actions has been rapid, and I continue to see month-over-month improvements.

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

Agentic Design Patterns Part 5, Multi-Agent Collaboration: Prompting an LLM to play different roles for different parts of a complex task summons a team of AI agents that can do the job more effectively.

Agentic Design Patterns Part 5, Multi-Agent Collaboration: Prompting an LLM to play different roles for different parts of a complex task summons a team of AI agents that can do the job more effectively.

Agentic Design Patterns Part 4, Planning: Large language models can drive powerful agents to execute complex tasks if you ask them to plan the steps before they act.

Agentic Design Patterns Part 4, Planning: Large language models can drive powerful agents to execute complex tasks if you ask them to plan the steps before they act.

Agentic Design Patterns Part 3, Tool Use: How large language models can act as agents by taking advantage of external tools for search, code execution, productivity, ad infinitum

Agentic Design Patterns Part 3, Tool Use: How large language models can act as agents by taking advantage of external tools for search, code execution, productivity, ad infinitum

Agentic Design Patterns Part 2, Reflection: Large language models can become more effective agents by reflecting on their own behavior.

Agentic Design Patterns Part 2, Reflection: Large language models can become more effective agents by reflecting on their own behavior.

Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance

Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance

Life in Low Data Gravity: With generative AI, data is bound less tightly to the cloud provider where it’s stored. This has big implications for developers, CIOs, and cloud platforms.

Life in Low Data Gravity: With generative AI, data is bound less tightly to the cloud provider where it’s stored. This has big implications for developers, CIOs, and cloud platforms.

The Dawning Age of Agents: LLM-based agents that act autonomously are making rapid progress. Here's what we have to look forward to.

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