Function Calling in LLMs Explained
Why it matters: Function calling in LLMs, explained clearly: how models call tools, why strict schemas hit 100% reliability, and the security risks you cannot ignore.
Sanksshep Mahendra
Sanksshep Mahendra is a technology executive with success in driving, vision, strategy, design, and execution of software engineering for the web, mobile, apps, social, voice, IoT, applications along with Machine learning and AI. His expertise lies in partnering with business leaders, powering through roadblocks, and leading global teams to deliver disruptive products that advance the organization’s mission and capture game-changing results in the market. Sanksshep Mahendra has a lot of experience in M&A and compliance, he holds a Master's degree from Pratt Institute and executive education from Massachusetts Institute of Technology, in AI, Robotics, and Automation.
LangGraph vs CrewAI vs AutoGen
Why it matters: LangGraph vs CrewAI vs AutoGen compared on control, pricing, benchmarks, and 2026 roadmaps, with a selector to pick the right AI agent framework.
Energy Efficient AI Training Techniques
Why it matters: Cut AI training energy without losing accuracy: compare mixed precision, quantization, pruning, distillation, and carbon aware scheduling with real numbers.
Semantic Knowledge Graph for LLM Agents
Why it matters: See how a semantic knowledge graph for LLM agents slashes hallucinations, adds memory, and lifted answer accuracy from 17 to 54 percent in tests.
Agentforce vs Microsoft Copilot Comparison
Why it matters: Agentforce vs Microsoft Copilot: real pricing, autonomy, security and ROI data to pick the right enterprise AI agent before you overspend.
Enterprise AI Cost Optimization Strategies
Why it matters: Cut enterprise AI spend 40 to 70 percent in 2026 using proven routing, caching, batching, and FinOps cost optimization strategies that protect quality.
AI Agent Memory Architecture
Why it matters: AI agent memory architecture explained: the four memory types, vector retrieval, and the frameworks that make agents actually remember.
Domain-specific AI Agents vs General Agents
Why it matters: Domain-specific vs general AI agents: real accuracy, cost, and ROI data plus named enterprise deployments to help you pick the right agent.
Context Rot in Large Language Models
Why it matters: Context rot makes LLMs fail long before the window fills. See why, how to measure it, and the context engineering fixes that keep answers reliable.
Post-training Quantization for Edge AI
Why it matters: Post-training quantization for edge AI: how INT8 and INT4 shrink models 4-16x, what accuracy costs, and how to deploy fast with GPTQ, AWQ, and GGUF.