Anand Houston
AI & Digital Marketing Specialist
Anand Houston is a digital marketer and AI developer who has been building revenue systems since 2017, from Facebook ad campaigns to full-stack AI applications. He is a digital marketing veteran turned AI engineer with experience scaling businesses through paid media, sales funnels, and data-driven strategy. Since 2022, he has focused on applied AI, building production automation, RAG pipelines, and agentic tools. He thoroughly tests every tool he writes about and brings a practitioner's perspective to each article, grounded in real implementation rather than theory.
Articles by Anand Houston
Ambient AI Explained: How Context-Aware AI Works and Where It's Used
Discover how ambient AI uses sensors, context awareness, and automation to assist people without prompts. Explore healthcare, IoT, security, and business use cases.
What Is Retrieval-Augmented Generation?
Learn how RAG helps AI find accurate answers from real data.
Grok vs ChatGPT: 2026 Comparison
Compare cost, coding, real-time data, safety, and visual AI features.
Best Claude Alternatives in 2026
Compare Claude alternatives for coding, research, writing, and AI productivity.
What Is Deterministic AI?
Definition, examples, and how it differs from generative AI.
What Is a Prompt in AI? A Beginner’s Guide to Prompt Engineering
Discover how prompts guide AI systems, why they matter, and how mastering prompting can unlock better answers from generative AI.
What Is Conversational AI? How AI Systems Hold Human-Like Conversations
Learn what conversational AI is, how it works, and how modern LLM-powered systems like ChatGPT transformed chatbots into intelligent assistants used in customer service, business automation, and more.
How to Write a Prompt: A Complete Guide to Effective AI Prompting
Master the fundamentals of effective prompting, avoid common errors, and learn how to refine prompts through testing and iteration.
What Is Generative AI? A Clear Guide to LLMs, Uses, and Limits
Learn how generative AI works, how LLMs create text and images, key use cases, and the risks—including hallucinations, bias, and accuracy limits.