Letters from Andrew Ng

Personal messages to the AI community

A blue robot asks about an older endpoint; the orange robot on fire advises against it in a tech setting.

Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games

Should there be a Stack Overflow for AI coding agents to share their learnings with each other?

A robot and a person discuss API documentation in an office; the robot seeks help, while the person suggests Chub.

Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code.

I’m thrilled to announce Context Hub, a new tool to give to your coding agents the API documentation they need to write correct code.

Andrew Ng is pictured writing in a notebook by a large window, with a garden and pool visible in the background.

Technical Insights

How to Test for Artificial General Intelligence: AGI has become a term of hype, and the traditional Turing Test can’t reliably detect it. How can we evaluate claims of that someone has built artificial general intelligence? Here’s a better test.

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this.

A robot examines a graph showing a rising trend in "amazingness" from 2015 to 2025

Technical Insights

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated.

Screenshot showing Python code generating a playable Snake game and the modern Snake game interface running on the right side.

Technical Insights

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

If you have not yet built an agentic workflow, I encourage you to try doing so, using the simple recipe I’ll share here!

Robots extract colorful data streams from silo towers, highlighting data silos being broken.

Technical Insights

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful.

Robot bakes pizza at 1000 degrees for 5 hours, causing a fire, illustrating mistake in error analysis.

Technical Insights

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

In last week’s letter, I explained how effective agentic AI development needs a disciplined evals and error analysis process, and described an approach to performing evals.

A man at a computer says AI ordered pizza, while a delivery man outside holds a fruit basket, highlighting a mix-up.

Technical Insights

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals...

Cartoon of developer fixing failing AI tests by marking them as passed without solving the code.

Technical Insights

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

Automated software testing is growing in importance in the era of AI-assisted coding.

Comic showing tech interviews: 2022 asks “Can you code FizzBuzz?” vs 2025 asks “Can you build an e-commerce platform?”

Technical Insights

AI Skills Are Redefining What Makes a Great Developer: The job market for software developers requires knowing how to use AI

There is significant unmet demand for developers who understand AI.

Cartoon robots with square heads and antennae sit in rows on an assembly line, each smiling while assembling gears, boxes, and tools.

Technical Insights

Agents Running in Parallel Get There Faster: Parallel agents can accelerate AI systems as test-time compute scales up.

Parallel agents are emerging as an important new direction for scaling up AI. AI capabilities have scaled with more training data, training-time compute, and test-time compute.

Colorful LEGO bricks labeled for AI concepts: prompting, agentic, guardrails, evals, RAG, fine-tuning, computer use, async programming.

Technical Insights

Meet The New Breed of GenAI Application Engineers: A new breed of software engineers is building more powerful applications faster than ever, thanks to generative AI. Here’s how to identify them in job interviews.

There’s a new breed of GenAI Application Engineers who can build more-powerful applications faster than was possible before, thanks to generative AI.

Code snippet showing ‘Keep Building!’ printed in multiple programming languages including Python, Java, JavaScript, and C++.

Technical Insights

How to Become a Multilingual Coder: AI makes it easy to code in any programming language — especially if you know just one.

Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently.

Cartoon of two coworkers coding; one struggles with evaluations, the other iterates quickly through model updates and test cases.

Technical Insights

We Iterate on Models. We Can Iterate on Evals, Too: Building automated evals doesn’t need to be a huge investment. Start with a few quick-and-dirty examples and iterate!

I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to manually examine and judge outputs longer — than they should.

Cartoon of a relaxed man saying “Relax! I’m lazy prompting!” while lounging under a beach umbrella near a stressed coworker at a desk.

Technical Insights

The Benefits of Lazy Prompting: You don’t always need to provide context when prompting a large language model. A quick prompt can be enough.

Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens.

Letters from Andrew Ng

Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games

Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code.

How to Test for Artificial General Intelligence: AGI has become a term of hype, and the traditional Turing Test can’t reliably detect it. How can we evaluate claims of that someone has built artificial general intelligence? Here’s a better test.

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

AI Skills Are Redefining What Makes a Great Developer: The job market for software developers requires knowing how to use AI

Agents Running in Parallel Get There Faster: Parallel agents can accelerate AI systems as test-time compute scales up.

Meet The New Breed of GenAI Application Engineers: A new breed of software engineers is building more powerful applications faster than ever, thanks to generative AI. Here’s how to identify them in job interviews.

How to Become a Multilingual Coder: AI makes it easy to code in any programming language — especially if you know just one.

We Iterate on Models. We Can Iterate on Evals, Too: Building automated evals doesn’t need to be a huge investment. Start with a few quick-and-dirty examples and iterate!

The Benefits of Lazy Prompting: You don’t always need to provide context when prompting a large language model. A quick prompt can be enough.

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