PCables AI Interconnects - Page 3

Government agencies are now procuring AI coding tools with strict SLAs and compliance requirements. Learn how contracts for AI CaaS differ from commercial tools, what SLAs are mandatory, and how agencies are avoiding costly mistakes in 2025.

Testing RAG pipelines requires both synthetic queries and real traffic monitoring. Learn how to measure retrieval, generation, cost, and latency-and turn production failures into better tests.

Private prompt templates are a critical but overlooked security risk in AI systems. Learn how inference-time data leakage exposes API keys, user roles, and internal logic-and how to fix it with proven technical and governance measures.

Value alignment in generative AI uses human feedback to shape AI behavior, making outputs safer and more helpful. Learn how RLHF works, its real-world costs, key alternatives, and why it's not a perfect solution.

Agentic generative AI is transforming enterprise workflows by autonomously planning and executing multi-step tasks without human intervention. Learn how it works, where it's used, and why it's not ready for everyone yet.

Learn how modern content moderation pipelines use AI and human review to block harmful user inputs to LLMs. Discover the best practices, costs, and real-world systems keeping AI safe.

Learn how streaming, batching, and caching reduce LLM response times. Real-world techniques used by AWS, NVIDIA, and vLLM to cut latency under 200ms while saving costs and boosting user engagement.

Learn how to accurately allocate LLM costs across teams using dynamic chargeback models that track token usage, RAG queries, and AI agent loops. Stop guessing. Start optimizing.

Generative AI is evolving into autonomous agents that plan, act, and adapt-driven by falling costs and better grounding in real data. Companies that adopt these systems now will lead the next wave of productivity.

Learn how to abstract LLM providers using proven interoperability patterns like LiteLLM and LangChain to avoid vendor lock-in, cut costs, and handle model switching safely. Real-world examples and 2025 best practices included.

Citations in RAG systems turn AI guesses into verifiable facts. Learn how to implement accurate, trustworthy citations using real-world frameworks, data prep tips, and the latest 2025 research to meet regulatory demands and build user trust.

Learn how calibration and outlier handling keep quantized LLMs accurate when compressed to 4-bit. Discover which techniques work best for speed, memory, and reliability in real-world deployments.