Production LLM Integration Checklist for Firebase & Serverless Backends

Modern LLM tooling makes it easy to ship a proof-of-concept chatbot.
What is still hard is running LLM-powered features in production on top of Firebase and serverless backends—without wrecking reliability or cost.

This checklist is what I use as an LLM integration consultant when helping Melbourne and Australia-based teams integrate LLMs, RAG pipelines, and AI agents into existing products.

If you are a CTO, staff engineer, or founder, you can use this as a high-level implementation guide and a review tool for work done by vendors or internal teams.

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1. Start with the backend, not the model

Most LLM failures I see in production are backend problems, not model choice.

• No clear separation between:
  • what runs in your trusted backend
  • what runs in the LLM sandbox
• No guardrails around outbound calls (LLM triggering workflows directly)
• No cost or latency budget

Before touching prompts:

1. Define the system boundary

   • What data does the LLM see?
   • What actions can it trigger?
   • What must always stay inside Firebase / GCP / AWS without going to the model?

2. Decide where state lives

   • Long‑term state: Firestore / Postgres / other DB
   • Retrieval state: vector store (pgvector, Pinecone, Qdrant, etc.)
   • Short‑lived orchestration state: serverless functions, queues, or a workflow engine

3. Make the LLM “just another service”

   • Wrap it behind a single internal client in your backend
   • Centralise auth, logging, retries, and provider switching there

If you cannot diagram this in 3–4 boxes, you are not ready to wire the LLM into user flows.

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2. Use a four-layer architecture

Production LLM systems work best when you keep concerns separate:

1. Data layer
   • Firestore / SQL for canonical data
   • Vector store for RAG (embeddings + metadata)
2. Model layer
   • Aggregated access to multiple providers (OpenAI, Anthropic, Google)
   • One config for model names, temperature, and max tokens
3. Orchestration layer
   • Stateless serverless functions, queues, scheduled tasks
   • Optional use of frameworks like LangChain / LangGraph for complex flows
4. Client layer
   • Web / mobile / internal tools
   • Handles streaming, incremental UI updates, and fallbacks

On Firebase / serverless, that typically looks like:

• HTTP callable functions or API routes as entrypoints
• Pub/Sub / queues for long‑running or multi‑step workflows
• Cloud Run or similar for heavier workers if needed

The key is that each layer is testable in isolation.

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3. Treat RAG as a data modelling problem

Retrieval‑augmented generation is where most production value sits—but only if retrieval is good.

Three practical rules:

• Chunk by meaning, not by token count
  • Use headings, sections, or semantic boundaries
  • Avoid dumping entire documents into a single vector
• Store rich metadata
  • Tenant / customer
  • Document type
  • Version / locale
  • Security level
• Hybrid search
  • Combine vector search with keyword / BM25 search
  • Rerank top candidates with a better model

On Firebase + serverless:

• Keep your source of truth in Firestore / storage
• Build and update vector indexes via background functions
• Use aliases / collections for zero‑downtime reindexing

If you cannot answer “what document and version did this answer come from?” you will have trust problems later.

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4. Make cost and latency first‑class citizens

LLM features die in production when:

• Latency spikes under load
• Bills are unpredictable

You can avoid that with some simple controls:

• Small models for small tasks
  • Classification, routing, extraction → cheaper models
  • Long‑form synthesis → larger models if needed
• Per‑tenant budgets
  • Track tokens per tenant, per feature
  • Rate‑limit or degrade gracefully when budgets are hit
• Timeouts at every boundary
  • Client → backend
  • Backend → LLM provider
  • Backend → vector store

On serverless, this also protects you from cold start surprises: if everything can time out cleanly, you can retry at the right level.

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5. Wrap tools and actions in explicit contracts

If your LLM can call tools (functions, APIs, workflows), treat those as a public contract:

• Input and output types live in code, not in prompts
• Each tool is idempotent or has explicit idempotency keys
• You log:
  • tool name
  • parameters
  • outcome (success / failure)

Examples of useful tools in a SaaS backend:

• create_support_ticket
• draft_email_reply
• flag_suspicious_payment
• summarise_conversation

From Firebase / serverless, these are just regular functions. The difference is that they are called via a strict, audited interface rather than ad‑hoc prompt text.

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6. Build an evaluation loop from day one

LLM quality drifts over time:

• Models change
• Your data changes
• Your users change

You need a repeatable evaluation process:

• Start with a seed set of:
  • realistic queries
  • expected behaviours (not always a single “correct” answer)
• Log user interactions into a structured store
• Periodically:
  • replay queries against your latest prompts / models
  • score for faithfulness and usefulness

Good metrics to track:

• Did the answer use the right sources?
• Did it hallucinate?
• Did the user escalate to a human or retry?

You can wire this into CI/CD: refuse to deploy a new model or prompt configuration if quality drops below a threshold.

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7. Security, compliance, and tenancy

For many Melbourne and Australia‑based companies, data jurisdiction and tenancy are critical.

Concrete practices:

• Namespace your vector store by tenant
• Encrypt at rest with keys that match your compliance story
• Redact or hash PII before sending it to external LLM providers
• Make it easy to:
  • delete a tenant’s data
  • rebuild only that tenant’s index

If you work in regulated industries (health, finance, education), this is non‑negotiable.

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8. A practical implementation path

If you are starting from an existing Firebase or serverless backend, a sensible path looks like:

1. Instrument your current system
   • Logging, tracing, and error visibility
2. Extract a single, narrow LLM use case
   • e.g. “summarise support conversations for internal use”
3. Design the four‑layer architecture
   • even if some layers are minimal at first
4. Ship a non‑critical internal version
   • iterate on prompts + retrieval
5. Harden
   • budgets, timeouts, retries, audit logging
6. Promote to user‑facing
   • with clear UX and fallbacks

From there, you can add additional workflows much faster, because the foundation is in place.

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Want a second set of eyes on your LLM architecture?

If you are planning or already running LLM features on Firebase, GCP, or other serverless backends, I can help you:

• stress‑test the architecture
• design a safer RAG pipeline
• control cost and latency before they explode

Request an architecture review →