Update basics (#13033)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

docs/basics/agent_context_engine.md

@@ -31,7 +31,7 @@ At its core, an Agent Context Engine is built on a triumvirate of next-generatio
 
 2. The Memory Layer: An Agent’s intelligence is defined by its ability to learn from interaction. The Memory Layer is a specialized retrieval system for dynamic, episodic data: conversation history, user preferences, and the agent’s own internal state (e.g., "waiting for human input"). It manages the lifecycle of this data—storing raw dialogue, triggering summarization into semantic memory, and retrieving relevant past interactions to provide continuity and personalization. Technologically, it is a close sibling to RAG, but focused on a temporal stream of data.
 
-3. The Tool Orchestrator: As MCP (Model Context Protocol) enables the connection of hundreds of internal services as tools, a new problem arises: tool selection. The Context Engine solves this with Tool Retrieval. Instead of dumping all tool descriptions into the prompt, it maintains an index of tools and—critically—an index of Playbooks or Guidelines (best practices on when and how to use tools). For a given task, it retrieves only the most relevant tools and instructions, transforming the LLM’s job from "searching a haystack" to "following a recipe."
+3. The Tool Orchestrator: As MCP (Model Context Protocol) enables the connection of hundreds of internal services as tools, a new problem arises: tool selection. The Context Engine solves this with Tool Retrieval. Instead of dumping all tool descriptions into the prompt, it maintains an index of tools and—critically—an index of Skills (best practices on when and how to use tools). For a given task, it retrieves only the most relevant tools and instructions, transforming the LLM’s job from "searching a haystack" to "following a recipe."
 
 ## Why we need a dedicated engine? The case for a unified substrate
 

docs/basics/rag.md

@@ -3,7 +3,7 @@ sidebar_position: 1
 slug: /what-is-rag
 ---
 
-# What is Retreival-Augmented-Generation (RAG)?
+# What is Retrieval-Augmented-Generation (RAG)?
 
 Since large language models (LLMs) became the focus of technology, their ability to handle general knowledge has been astonishing. However, when questions shift to internal corporate documents, proprietary knowledge bases, or real-time data, the limitations of LLMs become glaringly apparent: they cannot access private information outside their training data. Retrieval-Augmented Generation (RAG) was born precisely to address this core need. Before an LLM generates an answer, it first retrieves the most relevant context from an external knowledge base and inputs it as "reference material" to the LLM, thereby guiding it to produce accurate answers. In short, RAG elevates LLMs from "relying on memory" to "having evidence to rely on," significantly improving their accuracy and trustworthiness in specialized fields and real-time information queries.