You will get source citations and safe fallback added to your existing Python RAG app
Project details
Please message me before ordering so I can confirm your RAG flow, corpus, and package fit.
WHAT YOU GET
I will add working source citations and an evidence-aware fallback to one existing Python RAG flow. You get a focused code patch, citation metadata that reaches the answer, package-level evaluation questions, tests, and written run instructions. Work stays inside one codebase and one existing RAG application.
DONE WHEN
For the agreed sample corpus and questions, answerable cases return citations linked to their actual ingested source metadata, agreed unanswerable cases use the fallback instead of inventing support, and the included checks pass. I document how to reproduce the result and any remaining limits.
NOT INCLUDED
A new chatbot or RAG system, OCR or general document cleanup, vector-database or model-provider migration, full ingestion redesign, new authentication, production deployment, or guaranteed accuracy on arbitrary inputs. Never paste production credentials or confidential documents into chat; use redacted samples and an approved secure-access method.
WHAT YOU GET
I will add working source citations and an evidence-aware fallback to one existing Python RAG flow. You get a focused code patch, citation metadata that reaches the answer, package-level evaluation questions, tests, and written run instructions. Work stays inside one codebase and one existing RAG application.
DONE WHEN
For the agreed sample corpus and questions, answerable cases return citations linked to their actual ingested source metadata, agreed unanswerable cases use the fallback instead of inventing support, and the included checks pass. I document how to reproduce the result and any remaining limits.
NOT INCLUDED
A new chatbot or RAG system, OCR or general document cleanup, vector-database or model-provider migration, full ingestion redesign, new authentication, production deployment, or guaranteed accuracy on arbitrary inputs. Never paste production credentials or confidential documents into chat; use redacted samples and an approved secure-access method.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$179
|
Standard
$349
|
Advanced
$599
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
10 additional eval questions
(+ 1 Day)
+$80
Additional existing UI surface
(+ 2 Days)
+$120Frequently asked questions
About Chris
Production AI Engineer | RAG, AI Agents & Integration
Seoul, South Korea - 7:43 am local time
For founders and product teams, I build and repair RAG, AI-agent, and OpenAI/Claude systems with measured quality, cost controls, observability, and human approval for sensitive actions.
GOOD FIT IF YOU NEED
• A RAG flow with source citations, safe fallback, and an evaluation set
• A LangGraph/PydanticAI agent with tool calling and approval gates
• An OpenAI or Claude API issue fixed in Python/FastAPI
• One AI feature integrated into an existing Next.js product
PROOF
• 7+ years shipping applied AI; former founding CTO of a Seoul AI startup
• 60+ paid AI projects delivered through major Korean freelance marketplaces
• Cut a review-and-outreach workflow from ~60 minutes to ~5
• Improved multimodal retrieval speed by 98.4% and reduced GPU/database cost by 80%
• Shipped review-analysis models inside CJ OnStyle
• 6 registered patents and a peer-reviewed ML paper
HOW I WORK
We agree on the failure mode, scope, and proof of completion before I build. You receive reviewable increments, tests, clear tradeoffs, concise status updates, and handoff documentation.
Before hiring or ordering a Project Catalog package, send me an Upwork message with your current stack, what is failing or missing, and the outcome you need. I’ll confirm fit and recommend the smallest practical next step.
Steps for completing your project
After purchasing the project, send requirements so Chris can start the project.
Delivery time starts when Chris receives requirements from you.
Chris works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm the RAG flow
I review the code, sample corpus, metadata, question set, and selected package boundary.
Trace the evidence path
I follow source, page, and chunk metadata through retrieval and answer generation to locate the break.