Here's the thing nobody tells beginners You don't start your FDE journey with zero experience. You start it not knowing which of your existing experiences already count. That's a very different problem. And it's a much easier one to fix. 𝐓𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭 → Ever translate a confusing bug report into something the team could actually act on? That's discovery. → Ever sit with a frustrated user and figure out what they actually meant, not just what they typed in the ticket? That's requirements gathering. → Ever build a rough version of something just to prove an idea works, before anyone approved a "real" project? That's rapid prototyping. → Ever explain a technical decision to someone non-technical, in a way that actually landed? That's the single hardest FDE skill. And you've already done it. 𝐍𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐬𝐡𝐨𝐰𝐞𝐝 𝐮𝐩 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐫𝐞𝐬𝐮𝐦𝐞 𝐚𝐬 "𝐅𝐃𝐄 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞." Nobody labeled it. So you never counted it. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐢𝐭 𝐬𝐨𝐮𝐧𝐝𝐬 Most people delay starting because they think the gap is bigger than it is. "I've never worked directly with clients." "I don't have a customer-facing background." "I'm just a [tester / analyst / developer], I'm not there yet." But FDE isn't one skill you're missing. It's a handful of skills you've been practicing in pieces, in a different context, under a different job title. The work now isn't starting from scratch. It's connecting the dots you already have and building the few pieces you're actually missing. 𝐀 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 Before you touch a single course, do this: Write down 3 moments from your current or past role where you: - Figured out what someone really needed (not just what they asked for) - Built something fast, under pressure, without a perfect spec - Explained something technical to someone non-technical, and they got it If you can find even one of these, you're not starting at zero. You're starting at "I already have proof, I just never framed it this way." That reframe alone changes how you learn everything after it.

Happy to share that Agent is Ready and working is as expected... like Evalutaions and Guardrails etc... Concepts Covered: RAG LLM Embedding Chuncks.. Strealist Thanks to @Fde Vision for your support and assistance....

Shared this success in the public... please like it... URL: https://lnkd.in/p/dFqNAThT

Your agent can be: ✅ Correct ✅ RAG working ✅ Intent routing working ✅ UI working and still have: ❌ Rate-limit problem ❌ No retry strategy ❌ No caching ❌ No monitoring ❌ No concurrency strategy

In my previous post, I shared a real lesson from building my AI Agent: Functional correctness ≠ Production readiness. The agent was working correctly — RAG, intent routing, evaluations, guardrails and UI were all functioning. Then I hit: 429 — RESOURCE_EXHAUSTED The API quota had been exceeded. That created a new engineering question: What should an AI application do when the model/API temporarily refuses a request? The first solution I implemented For my current evaluation/testing scenario, I introduced a controlled delay using Python's: time.sleep() Instead of continuously firing requests, the test execution pauses between calls. Conceptually: Request → Wait → Request → Wait → Request This helped me avoid sending requests too aggressively during automated evaluation. And it solved the immediate problem in my development/testing environment. #But the bigger #FDE lesson was not time.sleep(). It was understanding rate limiting and resilience. An AI application needs to consider: 🔹 API quotas 🔹 Request frequency 🔹 Retry behavior 🔹 Exponential backoff 🔹 Concurrency 🔹 Caching 🔹 Token consumption 🔹 Monitoring & observability 🔹 Graceful failure / fallback Because at scale, this becomes both a technical problem and a business problem. 💰 Think about it from a customer perspective Imagine an application receiving: 10 requests → Fine 100 requests → More API calls 1,000 requests → Higher token/API consumption 10,000 requests → Quota, latency, concurrency and cost become serious considerations So the FDE question isn't simply: “Can the AI answer the question?” It's: “Can the AI solution continue to provide a reliable customer experience when usage increases or external services become constrained?” That's a completely different level of thinking. My current learning