You will get RAG System Audit & Optimization — Fix an Underperforming Pipeline
Project details
Built a RAG system that isn't returning good answers? You're not alone — plenty of pipelines shipped fast and now underperform. This audit pinpoints why and delivers a ranked fix list.
The audit covers your full retrieval pipeline: embedding strategy, document chunking, and prompt templates. You receive a written report with five specific improvements ranked by impact, so it's clear what to change first. Higher tiers go beyond the report and implement the fixes for you — the single highest-impact fix (Standard) or the top three (Advanced).
An audit-first approach shows real improvement before you commit to a full rebuild — and gives you a clear technical path either way.
The audit covers your full retrieval pipeline: embedding strategy, document chunking, and prompt templates. You receive a written report with five specific improvements ranked by impact, so it's clear what to change first. Higher tiers go beyond the report and implement the fixes for you — the single highest-impact fix (Standard) or the top three (Advanced).
An audit-first approach shows real improvement before you commit to a full rebuild — and gives you a clear technical path either way.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, BERT, ChatGPT, GitHub Copilot, MLflow, NLTK, NumPy, pandas, Python, PyTorch, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$500
|
Standard
$900
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 1 | 1 |
Number of Model Variations | 1 | 2 | 4 |
Number of Scenarios | 5 | 10 | 15 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - |
Optional add-ons
You can add these on the next page.
Add & Tune a Reranker
(+ 5 Days)
+$600
Embedding Model Migration
(+ 5 Days)
+$500
Production Deployment Support
(+ 7 Days)
+$800Frequently asked questions
About Carlos
Senior AI Engineer | LLM Apps, RAG, AI Agents, MLOps
Hollywood, United States - 7:34 pm local time
I work with startups, scale-ups, and enterprise teams to turn complex business problems into intelligent, reliable AI systems — whether that's a knowledge assistant, a document intelligence tool, a multi-agent workflow, or a user-facing AI product shipped end-to-end.
What I build:
+ RAG pipelines and semantic search systems over proprietary data
+ AI agents and multi-step automation workflows with tool integrations
+ LLM-powered applications (chatbots, copilots, Q&A, summarization, extraction)
+ FastAPI and Python backend services for AI features
+ Full-stack AI products with React/Next.js frontends
+ AI platform infrastructure, model serving, and cloud deployments
How I work:
I focus on complete, production-ready systems — not just model integrations. That means clean architecture, reliable retrieval, grounded outputs, and AI that actually fits into how your team or users operate. I care about accuracy, maintainability, and measurable impact.
Tech I work with regularly:
Python · FastAPI · LangChain · LangGraph · LlamaIndex · OpenAI API · Anthropic Claude · HuggingFace · PostgreSQL · pgvector · Pinecone · FAISS · Weaviate · React · Next.js · Docker · AWS · CI/CD
If you're building an AI system and need someone who can own it from architecture to delivery — let's connect!
Steps for completing your project
After purchasing the project, send requirements so Carlos can start the project.
Delivery time starts when Carlos receives requirements from you.
Carlos works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & Access Review
Review of your RAG system, sample queries, and pipeline access to scope the audit.
Pipeline Audit
Assessment of retrieval, embeddings, chunking, and prompt templates against your sample queries.