You will get ML Model Fine-Tuning Assessment — Fine-Tune, Prompt, or RAG?
Project details
Should you fine-tune a custom model, lean on prompt engineering, or use RAG? Picking wrong is expensive. This assessment gives you a clear, evidence-based answer before you spend on training.
The assessment reviews your use-case requirements, evaluates whether your data is actually ready for fine-tuning, runs a cost/benefit analysis of fine-tuning versus the alternatives, and recommends specific models (API-based or open-source). You receive a written report with a straightforward go/no-go recommendation. Higher tiers go further — Standard prepares your training dataset, and Advanced runs a pilot fine-tune with real evaluation metrics so you can see results before committing to a full project.
As teams move beyond basic API calls, fine-tuning is increasingly the right call — but only when the data and economics line up. This tells you whether they do.
The assessment reviews your use-case requirements, evaluates whether your data is actually ready for fine-tuning, runs a cost/benefit analysis of fine-tuning versus the alternatives, and recommends specific models (API-based or open-source). You receive a written report with a straightforward go/no-go recommendation. Higher tiers go further — Standard prepares your training dataset, and Advanced runs a pilot fine-tune with real evaluation metrics so you can see results before committing to a full project.
As teams move beyond basic API calls, fine-tuning is increasingly the right call — but only when the data and economics line up. This tells you whether they do.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, BERT, ChatGPT, Databricks Platform, MLflow, NumPy, pandas, Python, PyTorch, Sonnet, SQLWhat's included
| Service Tiers |
Starter
$400
|
Standard
$1,200
|
Advanced
$2,400
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 19 days |
Number of Revisions | 1 | 1 | 1 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 3 | 5 | 8 |
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.
Additional Model Comparison
(+ 10 Days)
+$1,500
Data Annotation & Labeling
(+ 5 Days)
+$600Frequently asked questions
About Carlos
Senior AI Engineer | LLM Apps, RAG, AI Agents, MLOps
Hollywood, United States - 6:27 am 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.
Use-Case, Data & Goals Review
Review of your use case, data, and success criteria to scope the assessment.
Readiness & Cost/Benefit
Data-readiness assessment and a cost/benefit analysis of fine-tuning vs. prompting/RAG.