Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Understand what LLM calibration means, how to measure it with calibration curves, and practical techniques like temperature scaling and verbalized confidence to build agents that know when they do not know.
Understand how Mixture of Experts architectures work, how token routing and expert capacity affect performance, and what MoE models mean for designing efficient agentic systems.
Learn how speculative decoding uses lightweight draft models to generate candidate tokens that a large target model verifies in parallel, achieving 2-3x inference speedups without quality loss.
Learn how prefix tuning and soft prompts let you customize LLM behavior by training small continuous vectors prepended to model inputs, achieving fine-tuning-level performance at a fraction of the cost.
Learn techniques for detecting and repairing common LLM output problems including truncated responses, malformed JSON, encoding artifacts, and broken code blocks through robust post-processing pipelines.
Learn how context distillation compresses lengthy documents, conversation histories, and knowledge bases into compact representations that preserve essential information while dramatically reducing token costs.
Design a production-grade AI agent marketplace with catalog management, semantic search, automated provisioning, and usage-based billing. Learn the core data models and API patterns that power agent distribution at scale.
Learn how to package AI agents into portable, versioned bundles with dependency management, configuration schemas, and reproducible deployments. Build a packaging format that works across teams and environments.
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