Study prompt, context, harness, loop, and graph engineering alongside current agent architectures, evaluation methods, and fast-moving AI technology.
Our approach
Exalted AI is for practitioners who already know the field exists and want to keep moving. It focuses on LLMs, agents, context, evaluation, retrieval, tools, memory, reasoning, model behavior, and the surrounding harness—not because every topic is brand-new research, but because these ideas are still new enough to reshape ordinary software engineering when you understand the mechanism behind them.
Editorial standards
Advanced AI engineering tutorials on prompting, context, agent harnesses, feedback loops, graph engineering, and model behavior.
Engineering tracks
Move across prompt, context, harness, loop, graph, Python, and current AI technology topics according to the system layer you need to understand.
Design prompts, examples, output contracts, and evaluations for reliable model behavior.
Go learning pathControl retrieval, memory, routing, compression, and the information reaching each model call.
Go learning pathBuild tools, state, execution, authorization, recovery, and observability around AI models.
Go learning pathDesign bounded agent feedback loops with progress checks, state, budgets, and termination.
Go learning pathCoordinate agents, state, dependencies, communication, and control with graph-based system design.
Go learning pathMechanism-first explainers for important AI projects, protocols, research, and architecture debates.
Go learning pathFeatured tutorials
These articles focus on architecture, state, context, feedback, evaluation, and practical consequences rather than launch-demo vocabulary.
An agent that has run forty steps and produced an answer no better than step three is not stuck. It is broken in a specific, nameable way.
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“Everything is a plugin” is a claim about architecture, not a feature list. It says the agent loop itself is replaceable — and that is expensive to mean.
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Two teams ship the same model, the same prompt, the same tool list. One agent finishes a multi-step task. The other stalls, loops, and quietly corrupts its…
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The prompt you spent a week tuning is not what the model reads. It reads everything in the window — and everything else is quietly voting.
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The demo works. Then you add a second worker, a shared artifact store, and a verifier, and the system starts producing results nobody can attribute,…
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A prompt is a spark, not an engine. The spark still matters — it just cannot carry the machine.
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Fresh technical explanations of LLM systems, agents, tooling, evaluation, and emerging engineering patterns.
MCP answers "what can I call?" A2A answers "who can I hand this to?" Different questions, different failure modes.
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A dict payload that passes every test until someone renames a key. A callback whose signature drifts by one argument. A duck-typed object that satisfies…
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Your recall metric looks healthy. Your answers are still wrong. That gap is the whole reason advanced retrieval patterns exist.
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The demo worked because the agent was running on your laptop. It had your credentials, your network, your home directory, and your shell history. Move it…
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The demo agent works. It reads the file, runs the test, patches the bug. Then it ships to a second tenant, the session runs for six hours, the process dies…
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A prompt fix that passes the demo and fails two weeks later is not a bad prompt. It is an improvement process with no memory.
Read tutorialConnect advanced AI engineering with algorithm practice, Python, machine learning, reinforcement learning, and practical LLM foundations.
Algorithm and data-structure practice with explicit reasoning, implementation patterns, and interview-oriented solutions.
Beginner-friendly Python tutorials, examples, and learning paths for practical programming foundations.
Practical machine-learning tutorials covering classical methods, data workflows, evaluation, and deployment foundations.
Reinforcement-learning tutorials covering value methods, policy optimization, offline RL, and modern RL applications.
Practical LLM tutorials for builders who want to understand prompting, workflows, agents, and AI applications.
