RAG Chatbots: Copilot Studio vs Claude - Understanding Multi-Context AI Workflows


Introduction

Generative AI chatbots are transforming how we interact with information, making knowledge instantly accessible through natural language. A key technique behind these AI assistants is RAG (Retrieval-Augmented Generation), where a model retrieves relevant knowledge before generating answers.

But not all RAG implementations are created equal. Tools like Microsoft Copilot Studio and Anthropic’s Claude offer different ways of handling context, which significantly impacts how your AI agent consumes and reasons over information.

Copilot Studio : Single Unified Context

Copilot Studio allows you to connect multiple knowledge sources — APIs, document libraries, Dataverse tables, or other structured sources — to your AI agent. The agent then retrieves relevant content from these sources and generates responses.

How it works:

  • Multiple knowledge sources are merged into a single context.

  • The AI determines relevance and uses what it needs for each query.

  • There’s no concept of independent “tabs” or separate knowledge buckets.

Pros:

  • Simple setup for RAG with multiple data sources.

  • Effective for single-topic or unified workflows.

  • Retrieval and generation are streamlined for fast response.

Cons:

  • Knowledge sources are flattened into one pool; unrelated content may mix.

  • Limited flexibility if you want independent reasoning per dataset or topic.

Claude : Multi-Context Tabs for Flexible AI Workflows

Claude (especially Claude Code) introduces the concept of multiple context tabs, allowing each dataset or knowledge source to exist in its own independent space.

How it works:

  • Each context tab can represent a separate API, document set, or project.

  • The AI can reason within each tab independently or combine insights across tabs.

  • Users can organize complex projects with modular context spaces.

Pros:

  • Perfect for multi-topic workflows or large datasets.

  • Prevents cross-contamination of knowledge from unrelated sources.

  • Supports advanced project or research-style reasoning.

Cons:

  • Requires more planning to organize contexts effectively.

  • May be overkill for simple, single-topic RAG setups.

Key Differences - Visual Summary

FeatureCopilot StudioClaude (Code)
Multiple Context Tabs❌ Not supported✅ Supported
Multiple Knowledge Sources✅ Supported✅ Supported
Unified RAG Response✅ Yes✅ Yes
Independent Reasoning per DatasetLimited✅ Yes

In Copilot Studio, all sources feed into one retrieval pool. Claude allows “separate workspaces” that maintain independent reasoning contexts.

Which Approach Should You Use?

  • Copilot Studio: Ideal for enterprise environments with structured APIs and document libraries, where a unified knowledge pool is sufficient.

  • Claude Multi-Tab Contexts: Best for research-heavy, multi-project, or multi-topic environments requiring modular knowledge management.

Both tools excel at RAG, but understanding the difference in context handling will help you choose the right AI architecture for your needs.

Conclusion

As AI adoption grows, context management becomes a critical factor in RAG chatbot effectiveness. Copilot Studio provides a streamlined, unified approach, while Claude’s multi-context tabs offer flexibility for complex workflows. Understanding these differences allows businesses and developers to design AI agents that are smarter, more accurate, and better organized.


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