Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Learn how Haystack's pipeline architecture and component-based design enable building production-grade NLP and agent systems with flexible routing, branching, and ready-made components.
Learn when and how to build agents using direct LLM API calls instead of frameworks, with a minimal implementation that demonstrates the agent loop, tool calling, and state management from scratch.
A practical decision matrix for selecting the right agent framework based on team size, use case complexity, scalability needs, vendor preferences, and production requirements.
Learn how to migrate between agent frameworks using abstraction layers, interface design, gradual migration strategies, and comprehensive testing to avoid costly full rewrites.
Learn how Tree-of-Thought prompting enables LLMs to explore branching reasoning paths, evaluate intermediate steps, and converge on higher-quality answers for complex problems.
Discover how self-consistency prompting improves LLM accuracy by sampling multiple reasoning paths and using majority voting to select the most reliable answer.
Explore meta-prompting techniques where LLMs generate, evaluate, and iteratively refine their own prompts, creating self-improving prompt optimization loops.
Learn how to design retrieval-augmented prompts that dynamically inject relevant context, manage context windows efficiently, and produce grounded answers from external knowledge.
