The AI landscape has shifted dramatically. While chatbots dominated for years, we’re now witnessing something far more powerful: autonomous AI agents that don’t just respond—they plan, execute, and accomplish goals. Chatbot vs AI Agent Aspect Chatbot AI Agent Purpose Respond to prompts Achieve goals autonomously Behavior Reactive (one-shot) Proactive (multi-step) Planning None Breaks goals into […]
Read more →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
DecSecOps: Integrating Security into DevOps – Part 9 – The Final – Application Security and Immutable Infrastructure for DevSecOps
This is a final series to conclude and summarize the key topics covered in previous 8 blogs: DevSecOps is an approach to software development that emphasizes integrating security into every stage of the software development lifecycle. Application security and immutable infrastructure are two key practices that can help organizations achieve this goal. Application Security Application […]
Read more →Token Optimization Techniques: Maximizing Value from Every LLM Token
Introduction: Tokens are the currency of LLM applications—every token costs money and consumes context window space. Efficient token usage directly impacts both cost and capability. This guide covers practical token optimization techniques: accurate token counting across different models, content compression strategies that preserve meaning, budget management for staying within limits, and prompt engineering patterns that […]
Read more →Prompt Versioning and Management: Bringing Software Engineering Rigor to LLM Development
Introduction: Prompts are code. They determine how your LLM application behaves, and like code, they need version control, testing, and deployment pipelines. Yet many teams treat prompts as afterthoughts—hardcoded strings scattered across the codebase, changed ad-hoc without tracking. This leads to regressions, inconsistent behavior, and difficulty understanding why outputs changed. This guide covers practical prompt […]
Read more →Chain-of-Thought Prompting: Unlocking LLM Reasoning with Step-by-Step Thinking
Introduction: Chain-of-thought (CoT) prompting dramatically improves LLM performance on complex reasoning tasks. Instead of asking for a direct answer, you prompt the model to show its reasoning step by step. This simple technique can boost accuracy on math problems from 17% to 78%, and similar gains appear across logical reasoning, code generation, and multi-step analysis. […]
Read more →Prompt Compression: Fitting More Context into Your Token Budget
Introduction: Context windows are precious real estate. Every token you spend on context is a token you can’t use for output or additional information. Long prompts hit token limits, increase latency, and cost more money. Prompt compression techniques help you fit more information into less space without losing the signal that matters. This guide covers […]
Read more →