https://www.rasikjain.com/tags/ai-engineering/ Recent content in ai-engineering on Rasik Jain - Senior Architect | Generative AI & Full-Stack Engineering Hugo -- gohugo.io en Wed, 05 Aug 2026 00:00:00 +0000 https://www.rasikjain.com/posts/building-llm-application-dont-skip-guardrails-layer/ Wed, 05 Aug 2026 00:00:00 +0000 https://www.rasikjain.com/posts/building-llm-application-dont-skip-guardrails-layer/ When people talk about building LLM applications, the conversation usually starts with prompts. If the responses aren’t good enough, we improve the prompt. Then comes RAG and AI agents. All of these matter. But there’s another layer that often gets overlooked: Guardrails Think about a code review. Before code is merged into production, it gets reviewed. Not because developers write bad code, but because even experienced engineers miss edge cases or overlook security issues. https://www.rasikjain.com/posts/beyond-prompts-rise-of-ai-agent-memory/ Thu, 09 Jul 2026 00:00:00 +0000 https://www.rasikjain.com/posts/beyond-prompts-rise-of-ai-agent-memory/ For the past couple of years, the AI conversation has been all about prompt engineering. We experimented with different prompting techniques to get better responses from large language models. But as AI agents become capable of handling more complex tasks, the challenge is no longer writing the perfect prompt. It’s helping the agent remember and plan. Think about assigning an AI agent a task like researching competitors, creating a project plan, writing documentation, and tracking progress over several days. https://www.rasikjain.com/posts/agentic-ai-new-software-architecture/ Mon, 20 Apr 2026 00:00:00 +0000 https://www.rasikjain.com/posts/agentic-ai-new-software-architecture/ Agentic AI is quietly becoming the new software architecture. Not long ago, most GenAI apps were simple: prompt in, response out. Now, we are seeing something different: systems made up of multiple AI agents working together. These agents can plan tasks, call APIs, retrieve data, and coordinate with other agents to complete workflows, with minimal human input. A big shift for developers Apps are becoming goal-driven instead of prompt-driven AI is moving into the application’s control flow Multi-agent systems are replacing single LLM pipelines Memory, planning, and tool-use are becoming core components New engineering challenges But this also introduces a new set of engineering challenges: