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This article outlines optimal fixed-size chunking strategies for OpenAI-based Retrieval-Augmented Generation (RAG) systems using SaaS product documentation. It categorizes common documentation types—API references, tutorials, onboarding guides, concept overviews, and FAQs—and recommends chunk sizes, overlap, and expected chunk counts for each, all within OpenAI’s token constraints. Smaller chunks enhance precision for structured docs like APIs and FAQs, while larger chunks preserve context in tutorials and conceptual guides. Strategic overlap ensures continuity without redundancy. The study also offers guidance on improving documentation structure to enhance AI retrieval accuracy, ensuring effective support chatbots and file search experiences built on OpenAI models.

When using LLMs with external tools, the tool_choice parameter determines if and how tools are called. Options include "none" (disable tools), "auto" (model decides), "required" (at least one tool must be used), and forcing a specific tool. Each has distinct behaviors and suits different use cases—from flexible chatbots to compliance-critical systems. Proper use requires clear system instructions and well-defined tool descriptions. Additionally, enabling parallel_tool_calls allows multiple tools to be invoked in one step. Choosing the right configuration ensures accurate, efficient, and context-aware tool use, making it essential for building reliable AI-driven workflows and assistants.

The era of traditional SEO is ending as AI-powered search transforms discovery. Success now depends on reference rates—how often AI models cite your content—rather than search rankings. This comprehensive guide covers Generative Engine Optimization (GEO) strategies, brand perception management within AI systems, emerging monitoring tools, and practical steps to ensure AI accurately recommends your products in the new search landscape.

The OpenAI Responses API replaces the Assistants API with a simpler, more flexible architecture for building AI agents. It eliminates threads by using previous_response_id to maintain conversation context, supports stateless instructions, and improves tool usage through built-in integrations like file search, web search, and code execution. Developers benefit from strong TypeScript support, easier state management, and new features like vector stores with batch uploads and metadata. Migration is straightforward and preserves existing functionality, while paving the way for future enhancements. With OpenAI planning full feature parity and eventual deprecation of the Assistants API, switching now ensures long-term compatibility.

The blog compares OpenAI’s Responses API and Assistants API for tool-using AI agents. Responses API offers fine-grained control with manual orchestration, ideal for flexible, low-level implementations. Assistants API provides a structured, high-level framework with built-in state management and easier tool chaining, simplifying development at the cost of reduced flexibility.