Latest posts
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AI System Design: How to Build Reliable AI Systems End to End
AI system design begins with a question that is easy to overlook when working with individual prompts or model calls: what determines whether an entire system succeeds when it is made up of multiple steps? The answer is not necessarily the quality of any single component. A system can contain individually capable prompts, models, retrieval…
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AI Output Consistency: How Constraints Make LLM Workflows More Reliable
AI output consistency is not simply a matter of making a model “answer the same way” every time. A language model generates text by selecting among possible next tokens, and when an input permits several plausible continuations, different outputs can emerge even when the underlying task appears unchanged. Some of that variation comes from the…
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AI Agents: How AI Systems Reason, Act, and Adapt
AI agents take the capabilities of language models and connect them into a system that can work toward a goal over multiple steps. A language model can generate a response. A retrieval system can bring external information into context. Tools can allow a system to interact with external services, while memory can preserve useful information…
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LLM Memory: How AI Systems Remember Across Conversations
One of the most common misunderstandings about LLM memory is assuming that a language model simply remembers everything it has seen before. Mechanically, it doesn’t. If a model generates a response based only on its current input, information from earlier interactions has no direct influence on the next response unless that information is provided again.…
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AI Tool Calling: How LLMs Use Tools to Take Actions
A language model can generate text, but by itself it cannot send an email, query a database, check the current weather, or run a piece of code. So how can an AI system move beyond generating text and actually take actions? The answer is AI tool calling. Tool calling gives a language model a structured…
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How Retrieval-Augmented Generation (RAG) Uses Context to Improve LLM AnswersHow AI Answers Questions It Was Never Trained On
If a language model only generates responses from what it learned during training and the text in its current input, how can it answer questions about new, highly specific, private, or otherwise unfamiliar information? It can’t reliably do so unless that information is provided to the model at generation time. This is the fundamental idea…
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Multi-Step AI Workflows: Why Complex Tasks Need Decomposition
Why can a single prompt fail on a complex task, even when the instructions are clear and well-written? Because complex tasks are rarely single operations. They are usually combinations of multiple distinct steps: understanding the problem, identifying relevant information, organizing it, reasoning about it, and producing the final output. When all of these operations are…
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Prompt Engineering: How Context Shapes AI Responses
One of the most common misconceptions about AI is that prompt engineering is about finding the perfect command. It is easy to imagine a language model as a machine waiting for instructions: tell it what to do, and it executes the request. The better the instruction, the better the result. But that is not quite…
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How Large Language Models Generate Text: Tokens, Context, and Prediction
One of the most confusing things about modern AI is that the same language model can give completely different answers to what appears to be the same question. Ask it one way and you might get a detailed explanation. Ask it slightly differently and the tone changes. The structure changes. Sometimes even the conclusion changes.…
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How AI Learns: How Language Models Learn From Data
One of the strangest things about modern AI is that a remarkably simple training objective can produce surprisingly capable systems. Predict what comes next. That’s the basic idea behind how AI learns and the training objective used by many modern language models. Not explicitly solve math problems.Not manually learn grammar rules.Not receive a database of…