Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Learn to identify and fix performance bottlenecks in AI applications using cProfile, py-spy, memory profiling, and optimization strategies for LLM pipelines and data processing.
Master Python type hints for AI engineering including generics, TypedDict, Protocol classes, and runtime validation to build maintainable agent applications with self-documenting interfaces.
Quantization enables deploying large language models on constrained hardware by reducing numerical precision. Learn about FP4, FP8, INT8, and GPTQ techniques with practical accuracy trade-off analysis.
Quantum-classical hybrid computing combines quantum processors with AI to tackle problems beyond classical reach. Explore how hybrid approaches advance simulation, optimization, and cryptography.
Multimodal AI models that process text, images, audio, and video within a single architecture are redefining application possibilities. Explore vision-language models, audio processing, and unified architectures.
Learn how to evaluate RAG pipelines systematically using the RAGAS framework, measuring faithfulness, answer relevancy, context recall, and context precision to identify and fix retrieval failures.
Learn how to use metadata filtering in RAG to narrow vector search results using structured attributes like document type, date ranges, departments, and access levels for more precise retrieval.
Learn practical techniques to dramatically reduce RAG pipeline latency including async retrieval, semantic caching, pre-computation, and embedding optimization without sacrificing answer quality.
Learn how to build hybrid RAG systems that combine document retrieval with SQL database queries and API calls, unifying structured and unstructured data in a single pipeline.
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