What My RAG Experiment Taught Me About Platform Engineering for AI Workloads
I didn’t set out to become an AI architect.
I just wanted a smart assistant that could answer questions using my own blogs, interviews, and podcasts.
But somewhere between “upload your documents” and “get answers,” I ran headfirst into the reality every platform engineer will eventually face:
🛠️ You’re not just enabling AI—you’re being asked to productionize judgment.
🧠 TL;DR for Platform Engineers:
Here’s what I learned building a real-world RAG system on top of OpenAI’s stack—and what you should consider before deploying LLMs across your org:
🔍 1. Retrieval Systems Are Your New API Layer
Most LLMs are only as smart as their search index.
Even with clean, structured documents, my model often failed to surface strategic context. Why? Because it prioritized literal phrase matching over semantic intent.
Platform takeaway:
Invest in your retrieval stack like it’s a backend service.
Use hybrid search (vector + keyword)
Curate for intent, not just text
Test retrieval with business-critical queries
If the wrong result costs trust, this isn’t just “search”—it’s a platform responsibility.
🧱 2. Structured Content = Infrastructure
You wouldn’t ship untested code. So why ship unstructured content?
I uploaded an NDJSON file of my blog corpus—titles, tags, links, and summaries. The structured format helped reduce hallucination significantly.
Platform takeaway:
Treat knowledge assets like artifacts.
Define schemas
Enforce metadata standards
Version control your corpus (GitHub for content?)
The future of AI infrastructure includes pipelines that ship knowledge as reliably as we ship containers.
🔐 3. Boundaries Are Harder Than They Look
Despite strict prompts—“only answer from these documents”—the model still reached beyond the fence.
Sometimes it pulled from training data. Sometimes it invented facts.
Platform takeaway:
You can’t rely on prompt boundaries alone.
Enforce guardrails in code, not just text
Consider local inference for full control
Build validation layers before you hit production
If governance matters, you need to treat AI boundaries like firewall rules.
🧰 4. RAG Pipelines Aren’t Plug-and-Play
Most platform teams are being asked to “just integrate AI.”
But real RAG systems require glue code, corpus management, prompt engineering, and monitoring.
Platform takeaway:
You need new abstractions:
A content ingestion layer
A validation + ranking layer
Prompt + retrieval observability
Think “Kubernetes for knowledge flows.”
💡 5. Local vs. Cloud Is a Platform Decision
I prototyped my system on OpenAI—but quickly ran into cost, latency, and governance questions. Moving to Ollama + NVIDIA is now on the table.
Platform takeaway:
Architect around usage patterns.
Local: predictable cost, tight control
Cloud: rich context, faster iteration
Hybrid: the likely reality
Think beyond inference—optimize where and how AI workloads run.
👥 6. It’s Time to Rethink Platform Teams
RAG isn’t just infra + ML. It’s infra + ML + content strategy.
Platform takeaway:
You’ll need a cross-functional AI enablement pod:
Platform engineer (infra + hosting)
Data engineer (corpus + ETL)
Prompt/retrieval specialist (UX + tuning)
Domain validator (governance + trust)
If you don’t have this yet, start planning.
🧠 Final Thought:
You’re not just building AI infrastructure.
You’re building institutional memory at scale—and trust is your real SLA.
What started as a personal project turned into a roadmap for the next evolution of platform engineering.
CTA:
📘 Want to see how the architecture evolved?
What I Learned from Building a RAG-Based AI on My Own Work — And the Architectural Crossroads It Revealed
💬 Building something similar? Let’s trade notes.
📩 keith@advbench.com