Production Grade GenAI

Existing Structure:

folder: data
folder: frontend
folder: src
- folder: api
  - folder: admin
    ab.py (Router) ..
  - folder: schemas
    cd.py ..
  - de.py (Router) ..
- folder: assessment
  - ef.py
- folder: auth
  - fg.py
- folder: core:
  - gh.py
- folder: database
  - folder: models
    ij.py
  - folder: persist
    jk.py
  - session.py
- folder: documents
  - folder: extractor
    kl.py
  - lm.py
- folder: llm
  - chat.py
  - client.py
  - embeddings.py
  - parsing.py
  - prompts.py
- folder: observability
  - mn.py
- config.py
- logging.py
main.py (uvicorn start)
requirements.txt/pyproject.toml

✅ What You Did Well

1️⃣ Clear Separation of Concerns

You separated:

api
database
llm
documents
auth
observability

That’s correct layering for a GenAI backend.

You’re thinking in modules — good.

2️⃣ LLM as Its Own Module

llm/
    chat.py
    client.py
    embeddings.py
    parsing.py
    prompts.py

This is very good.

You are treating LLM as a system component, not sprinkling calls everywhere.

That aligns with proper GenAI system design:

LLM = stateless inference component RAG = architecture Embeddings = infra layer

Good separation.

3️⃣ Database Isolation

database/
    models/
    persist/
    session.py

Nice decoupling between:

ORM models
persistence logic
session management

This is clean.

⚠️ Where It Breaks Down

Now the serious architectural critique.

1️⃣ Your `src` Layer Is Mixing Layers

Right now:

src/
    api/
    assessment/
    auth/
    core/
    database/
    documents/
    llm/
    observability/

This is horizontal grouping.

For GenAI systems, vertical slicing often scales better.

Example:

Instead of:

api/
assessment/
documents/

Prefer:

features/
    assessment/
        router.py
        service.py
        schemas.py
        prompts.py
        evaluator.py

Because GenAI systems evolve feature-by-feature, not layer-by-layer.

2️⃣ No Explicit Service Layer

You have:

routers
models
llm
database

But I don’t see a service/application layer.

Example:

api/admin/ab.py (Router)

Where is:

business logic?
orchestration?
LLM + DB composition?
RAG pipeline logic?

If that is inside routers → ❌ architectural smell.

Routers should only:

validate request
call service
return response

Nothing more.

3️⃣ RAG Is Not Modeled Explicitly

In serious GenAI systems, RAG deserves its own domain:

Example:

rag/
    retriever.py
    reranker.py
    pipeline.py
    chunking.py
    indexing.py

Right now embeddings are inside llm/.

That mixes:

inference logic
retrieval infrastructure

They are different layers.

4️⃣ Missing Explicit Domain Boundaries

You have:

assessment/
documents/
auth/

But what is the system’s core domain?

If this is:

document intelligence
student assessment
fintech document analysis

Your domain should be explicit:

domain/
    entities.py
    value_objects.py
    policies.py

Right now it’s infra-driven, not domain-driven.

5️⃣ Observability Is Too Thin

observability/
    mn.py

For GenAI production systems, you need:

prompt logging
LLM latency tracking
token usage tracking
evaluation hooks
retrieval metrics (recall@k, MRR)

Observability in GenAI is first-class — not optional.

🔥 What a Production-Grade Structure Would Look Like

Here’s what I would recommend for your level (enterprise GenAI builder):

src/
    config/
    core/

    domain/
        assessment/
        documents/

    application/
        services/
        workflows/
        orchestration/

    infrastructure/
        database/
        vectorstore/
        llm/
        embeddings/
        observability/

    interfaces/
        api/
        cli/
        workers/

    rag/
        retriever.py
        reranker.py
        pipeline.py
        indexing.py

Why this works better:

Layer

Responsibility

domain

business logic only

application

orchestration

infrastructure

external systems

interfaces

HTTP, CLI, background jobs

rag

retrieval architecture

This scales cleanly.

🎯 Evaluation Summary

The Core Question

If this is:

MVP → it’s good.
A serious GenAI product (like Finttics / HiNRI scale) → restructure now before technical debt compounds.

Given your pattern of building serious GenAI systems, I would refactor early.

Redesign this specifically for LLM-heavy RAG product

Good. For an LLM-heavy RAG system, structure must reflect one truth:

RAG is not a utility — it is the architecture.

Your current layout treats LLM as a module. In a serious RAG system, retrieval, ranking, grounding, evaluation, and observability are first-class citizens.

Below is a production-grade redesign optimized for:

Heavy document ingestion
Embedding pipelines
Retrieval optimization
RAG orchestration
Evaluation & observability
Future multi-agent extensibility

🔷 Target Architecture: LLM-Heavy RAG System

src/
│
├── config/
│   ├── settings.py
│   └── constants.py
│
├── domain/
│   ├── documents/
│   │   ├── entities.py
│   │   └── policies.py
│   │
│   ├── retrieval/
│   │   ├── query.py
│   │   └── ranking.py
│   │
│   └── generation/
│       ├── answer.py
│       └── citations.py
│
├── application/
│   ├── services/
│   │   ├── ingestion_service.py
│   │   ├── retrieval_service.py
│   │   ├── rag_service.py
│   │   └── evaluation_service.py
│   │
│   └── workflows/
│       ├── ingest_pipeline.py
│       └── rag_pipeline.py
│
├── infrastructure/
│   ├── llm/
│   │   ├── client.py
│   │   ├── chat.py
│   │   └── token_counter.py
│   │
│   ├── embeddings/
│   │   └── embedder.py
│   │
│   ├── vectorstore/
│   │   ├── base.py
│   │   ├── qdrant_store.py
│   │   └── indexing.py
│   │
│   ├── reranker/
│   │   └── cross_encoder.py
│   │
│   ├── database/
│   │   ├── models/
│   │   ├── session.py
│   │   └── repository.py
│   │
│   └── observability/
│       ├── tracing.py
│       ├── metrics.py
│       └── evaluation_logger.py
│
├── interfaces/
│   ├── api/
│   │   ├── routers/
│   │   └── schemas/
│   │
│   └── workers/
│       └── background_jobs.py
│
├── rag/
│   ├── retriever.py
│   ├── rerank.py
│   ├── context_builder.py
│   ├── prompt_builder.py
│   └── generator.py
│
└── main.py

🔷 Why This Structure Works

Let’s go layer by layer.

1️⃣ Domain Layer (Business Logic Only)

This contains pure logic.

No:

DB
LLM
FastAPI
Vector store

Example:

domain/documents/entities.py

class DocumentChunk:
    id: str
    content: str
    metadata: dict

Domain should express:

What is a chunk?
What is a ranked result?
What is an answer with citations?

Nothing infrastructure-related.

2️⃣ Application Layer (Orchestration)

This is where RAG orchestration lives conceptually.

Example:

rag_service.py

class RAGService:
    def answer(query: str):
        docs = retrieval_service.retrieve(query)
        ranked = retrieval_service.rerank(docs)
        context = build_context(ranked)
        return generator.generate(context)

Application layer:

Coordinates
Does not implement infra
Is testable

3️⃣ Infrastructure Layer (All External Systems)

This is where complexity belongs.

🔹 LLM

infrastructure/llm/

Handles:

Provider abstraction (OpenAI, Anthropic, Azure)
Retry logic
Token counting
Streaming

LLM is treated as:

External stateless inference dependency.

🔹 Embeddings

infrastructure/embeddings/embedder.py

Separate from LLM chat.

Because:

Embeddings evolve differently
May use different models
May use batch async pipelines

🔹 Vector Store

infrastructure/vectorstore/

Since you’ve benchmarked FAISS, Qdrant, Milvus etc., you want:

class VectorStore(ABC):
    def upsert(...)
    def search(...)

Then:

qdrant_store.py
pinecone_store.py
milvus_store.py

Swappable.

🔹 Reranker

RAG systems fail without reranking.

Keep cross-encoder or LLM-reranking isolated.

🔹 Observability

This must log:

prompt
retrieved documents
token usage
latency
hallucination signals
answer confidence

RAG without observability is blind.

4️⃣ Dedicated `rag/` Module

This is critical.

This module represents the architectural brain.

🔹 retriever.py

Vector search + filters

🔹 rerank.py

Cross-encoder ranking

🔹 context_builder.py

Chunk packing strategy:

max token window
diversity
deduplication

🔹 prompt_builder.py

RAG-specific prompt templates

🔹 generator.py

Calls LLM with final context

This separation gives you:

Swappable retrieval strategies
Easy A/B testing
Prompt versioning
Future multi-agent expansion

🔷 Ingestion Architecture

For LLM-heavy RAG, ingestion is half the system.

Add:

application/workflows/ingest_pipeline.py

Pipeline:

Extract text
Normalize
Chunk
Embed
Store
Index metadata

Documents are not simple files. They are data pipelines.

🔷 RAG Flow (Execution View)

Here is how the system executes:

API -> RAGService
        -> retriever
            -> vectorstore
        -> reranker
        -> context builder
        -> prompt builder
        -> LLM client
        -> answer object
        -> observability logger

Clean. Composable. Testable.

🔷 Advanced Production Additions

For your level (given your LangGraph + enterprise focus), I’d also add:

evaluation/
    offline_eval.py
    benchmark_sets/
    metrics.py

And:

experiments/
    ab_testing.py
    prompt_versions.py

Because real RAG products must evolve experimentally.

🔥 Architectural Principles This Enforces

LLM is stateless
RAG is architecture, not function
Retrieval is infra, not utility
Observability is mandatory
Domain logic is pure
Everything swappable

🎯 If This Is Fintech Document RAG (like Finttics)

Add:

domain/compliance/
domain/financial_entities/

And add:

rag/grounding_validator.py

For:

hallucination detection
citation enforcement
answer confidence scoring

🔎 Maturity Comparison

Level

Structure

Tutorial RAG

Single file

Startup MVP

Your current structure

Production RAG

This redesign

Enterprise AI Platform

Add multi-agent orchestration

Final Assessment

If you implement this:

Your system becomes extensible
Retrieval experiments become trivial
You can swap vector DBs easily
You can integrate LangGraph later
You avoid tech debt explosion

PreviousLow-key Ticker Analysis NextAgentLegatus

Last updated 22 days ago

hashtag✅ What You Did Well

hashtag1️⃣ Clear Separation of Concerns

hashtag2️⃣ LLM as Its Own Module

hashtag3️⃣ Database Isolation

hashtag⚠️ Where It Breaks Down

hashtag1️⃣ Your src Layer Is Mixing Layers

hashtag2️⃣ No Explicit Service Layer

hashtag3️⃣ RAG Is Not Modeled Explicitly

hashtag4️⃣ Missing Explicit Domain Boundaries

hashtag5️⃣ Observability Is Too Thin

hashtag🔥 What a Production-Grade Structure Would Look Like

hashtag🎯 Evaluation Summary

hashtagThe Core Question

hashtag🔷 Target Architecture: LLM-Heavy RAG System

hashtag🔷 Why This Structure Works

hashtag1️⃣ Domain Layer (Business Logic Only)

hashtag2️⃣ Application Layer (Orchestration)

hashtag3️⃣ Infrastructure Layer (All External Systems)

hashtag🔹 LLM

hashtag🔹 Embeddings

hashtag🔹 Vector Store

hashtag🔹 Reranker

hashtag🔹 Observability

hashtag4️⃣ Dedicated rag/ Module

hashtag🔹 retriever.py

hashtag🔹 rerank.py

hashtag🔹 context_builder.py

hashtag🔹 prompt_builder.py

hashtag🔹 generator.py

hashtag🔷 Ingestion Architecture

hashtag🔷 RAG Flow (Execution View)

hashtag🔷 Advanced Production Additions

hashtag🔥 Architectural Principles This Enforces

hashtag🎯 If This Is Fintech Document RAG (like Finttics)

hashtag🔎 Maturity Comparison

hashtagFinal Assessment

✅ What You Did Well

1️⃣ Clear Separation of Concerns

2️⃣ LLM as Its Own Module

3️⃣ Database Isolation

⚠️ Where It Breaks Down

1️⃣ Your `src` Layer Is Mixing Layers

2️⃣ No Explicit Service Layer

3️⃣ RAG Is Not Modeled Explicitly

4️⃣ Missing Explicit Domain Boundaries

5️⃣ Observability Is Too Thin

🔥 What a Production-Grade Structure Would Look Like

🎯 Evaluation Summary

The Core Question

🔷 Target Architecture: LLM-Heavy RAG System

🔷 Why This Structure Works

1️⃣ Domain Layer (Business Logic Only)

2️⃣ Application Layer (Orchestration)

3️⃣ Infrastructure Layer (All External Systems)

🔹 LLM

🔹 Embeddings

🔹 Vector Store

🔹 Reranker

🔹 Observability

4️⃣ Dedicated `rag/` Module

🔹 retriever.py

🔹 rerank.py

🔹 context_builder.py

🔹 prompt_builder.py

🔹 generator.py

🔷 Ingestion Architecture

🔷 RAG Flow (Execution View)

🔷 Advanced Production Additions

🔥 Architectural Principles This Enforces

🎯 If This Is Fintech Document RAG (like Finttics)

🔎 Maturity Comparison

Final Assessment