What happens after you move from a prototype to an actual product? You know, with real users, weird inputs, bugs at 2 a.m., and a CFO breathing down your neck about token costs.
You Don’t Need a Bigger Model, You Need a Smarter Stack
Everyone’s obsessed with picking the “best” model. GPT-4, Claude, Gemini, Mistral, Llama, Llama++. But the truth? It’s not the model that makes or breaks your app — it’s how you orchestrate it.
Most successful LLM applications:
- Don’t rely on a single model.
- Use routing logic to pick models based on task or cost.
- Cache aggressively
- Handle errors gracefully.
Treat LLMs like cloud functions. Modular, replaceable, versioned. Make it easy to swap them out, A/B test, or even degrade to smaller ones when appropriate.
Prompt Engineering Is Dead. Long Live Prompt Infrastructure
You can’t hardcode prompts forever. One day, you’ll wake up with 63 brittle prompts duct-taped across your codebase like fragile little spells — and no idea what’s causing bugs.
What you need:
- Prompt template systems (with variables and formatting logic)
- Prompt version control
- A/B testing on prompts (yes, seriously)
- Prompt performance analytics
Build a prompt library as a first-class system, not an afterthought. Externalize it like copywriting.
Context windows are tiny. Your Users’ Expectations Aren’t.
The model forgets things. Your users don’t care.
That means you need:
- Vector databases (Pinecone, Weaviate, etc.)
- Metadata tagging
- Smart chunking (don’t feed it a whole doc — give it the right 3 paragraphs)
- Ranking and retrieval logic (RAG isn’t magic — it’s tuning, tuning, tuning)
Build a content pipeline. If your app pulls in documents, articles, or internal data, treat that ingestion and preprocessing as a core service, not a side feature.
Chain of Thought? More Like a Chain of Bugs
Let’s talk about orchestration. You want your model to do multistep reasoning, make API calls, and come back with answers like a tiny intern with Wi-Fi.
These systems are:
- Non-deterministic
- Expensive
- Debug — hell if you don’t log everything
Build a sandbox. Let your devs simulate chains, inspect intermediate outputs, and visualize tool calls. Log every thought. Literally. You’ll thank yourself later.
Observability Isn’t Optional — It’s Survival
Every LLM app needs what I call “AI Telemetry.” You wouldn’t ship a backend without logging, metrics, and error tracking — so why are you trusting a stochastic parrot to answer users without even logging its outputs?
Track:
- Inputs and outputs
- Latency and cost per call
- Model drift
- Prompt performance
- Feedback loops
Use observability tools built for LLMs (like Langfuse and PromptLayer) or roll your own. Pipe data into dashboards. Build alarms. Chaos test your own app.
Cost Controls Are a Feature, Not a Finance Problem
It’s dangerously easy to burn cash. One model call is cheap. A thousand nested ones? Suddenly, your free tier costs more than your cloud bill.
Add:
- Hard and soft rate limits
- Token tracking per user/request
- Model selection by priority/cost tier
- Usage analytics per endpoint
Make cost part of your dev workflow. Annotate endpoints with expected token usage. Log every call. Design for frugality like it’s a security principle.
Your model is stateless. Your users aren’t.
You’re not just building an app. You’re building memory.
That means you need:
- User profiles
- Long-term memory stores (for persistent facts/preferences)
- Session-level memory (for context continuity)
- Embeddings that evolve with the user
Separate memory from the session. Don’t jam it all into one prompt. Architect user memory as a first-class data layer — queryable, editable, and contextual.
The LLM Isn’t Your Product — The Architecture Is
At the end of the day, your app’s “smarts” aren’t in the model. They’re in:
- How do you route logic?
- How do you design failovers?
- How do you represent user needs?
- How you manage risk, cost, and context
LLMs are just the new compute layer. A very weird one. In the same way the cloud changed how we build infrastructure, LLMs are changing how we architect software. Your architecture is your differentiator. Treat it like one.
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