1046 posts · Page 19 of 88. ← Blog
Real tasks are pipelines, not single calls, and in DSPy you build them the way you build a neural network — subclass a base module, declare sub-modules, and wire them together in a forward method.
Real tasks are pipelines, not single calls. In DSPy you build them like a neural network — subclass a base module, declare sub-modules, and wire them together in a forward method.
The question this phase answers is simple and unforgiving — can you change the system without breaking it? — and the control you cannot skip is that no ungated change reaches users.
Can you change the system without breaking it? Phase 6: automated pipelines for code, models, and prompts, with eval gates, a registry, and automated rollback — no ungated change reaches users.
Real systems have more than one place to look, and the answer to "not everything should be retrieved from the same index — or retrieved at all" is to route queries and to treat retrieval as a tool the agent chooses to call.
Real systems have more than one place to look. The answer is to route queries to the right source — and to treat retrieval as a tool the agent chooses to call, or skips entirely.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions — and caching is how you stop paying full price for work you have already done.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions. Caching is how you stop paying full price for work you have already done.
A conversation that never forgets eventually overflows, so managing what history an agent carries forward — and how it remembers across sessions — is one of the defining problems of context engineering.
A conversation that never forgets eventually overflows, so managing what history an agent carries forward — and how it remembers across sessions — is a defining problem of context engineering.
A2A defines what agents exchange independently of how it travels, so the same operations work over JSON-RPC, gRPC, or plain REST — and the operation set is small enough to hold in your head.
A2A defines what agents exchange independently of how it travels, so the same operations work over JSON-RPC, gRPC, or plain REST — and the operation set is small enough to hold in your head.
Everything in the series so far comes together here: a small but complete Model Context Protocol server, in Python, exposing a tool, a resource, and a prompt, runnable and testable in minutes.
Build a complete MCP server in Python with the official SDK — a notes service with a tool, a resource, and a prompt — and test it end to end with the MCP Inspector.
The most ambitious form of self-evolution stops tweaking one agent and starts searching a space of many, letting a meta-process discover agent designs no human wrote.
The most ambitious self-evolution searches a population of agent designs. Automated Design of Agentic Systems, evolutionary prompt search, debate, and self-play.
When there's no reference answer and no rule to check, you can ask a strong model to grade the output — LLM-as-a-judge. It's the technique that made open-ended evaluation practical at scale, and it's also a minefield: judges have systematic biases, can be gamed, and agree with humans only when you design the grading carefully. Used well it's indispensable; used naively it produces confident numbers that mean nothing.
When there's no reference and no rule to check, you can ask a strong model to grade the output — LLM-as-a-judge. It made open-ended evaluation practical at scale, and it's a minefield: judges have systematic biases (position, verbosity, self-preference), can be gamed, and agree with humans only when you design the grading carefully.
A public key lets anyone encrypt to you and verify your signatures — but it raises a new question: how do you know a public key really belongs to who it claims? If an attacker can substitute their own public key for the bank's, all the cryptography in the world protects your connection to the attacker. Solving "whose key is this?" is what certificates, certificate authorities, and chains of trust exist for — the plumbing that makes public keys into verifiable identities.
A public key lets anyone verify your signatures — but how do you know a public key really belongs to who it claims? If an attacker substitutes their key for the bank's, all the cryptography protects your connection to the attacker. Certificates, CAs, and chains of trust solve 'whose key is this?'
Before any tactic, know the map. Four cloud and industry frameworks now govern AI cost — AWS, Azure, Google, and the FinOps Foundation — and they converge more than they differ. Every specific lever in this playbook sits underneath one of them, so starting with the constitutional documents is what turns a bag of cost tricks into a coherent discipline.
Before any tactic, know the map. Four cloud and industry frameworks now govern AI cost — AWS, Azure, Google, and the FinOps Foundation — and they converge more than they differ. Every specific lever sits underneath one of them.
LangChain is the framework everyone starts with and everyone has opinions about — a vast toolkit for building LLM applications whose real value isn't any one feature but the standardization it brings: one interface across every model, vector store, and tool, so you write your application once and swap the pieces underneath. Understanding what it is (and its relationship to LangGraph) cuts through the confusion.
LangChain is the framework everyone starts with and everyone has opinions about — a vast toolkit whose real value is the standardization it brings: one interface across every model, vector store, and tool, so you write your application once and swap the pieces underneath.