You will get a deep audit and optimization of your AI LLM or RAG system
Project details
Most AI chatbots and RAG systems fail silently. They hallucinate citations, generate noise instead of insights, and break under edge cases. The root problem is never "a bad prompt" — it is a structural architecture flaw that no amount of prompt tweaking can fix.
I am an AI Systems Engineer and Physicist-Mathematician with production experience integrating LLMs (Gemini, GPT-4, LLaMA) into real-world SaaS platforms. My approach to AI is scientific: I don't "vibe-check" outputs — I run systematic evaluations using the RAG Triad (Context Relevance, Groundedness, Answer Relevance) measured with Python frameworks like TruLens against a golden dataset.
In the Starter tier, you receive a PDF report diagnosing your top 3 architecture issues and how to fix them. In the Standard tier, I audit your full pipeline: Vector DB strategy, chunking logic, prompt chain, and output parsing. In the Advanced tier, I deliver the report AND refactor the code — migrating fragile Markdown parsing to Strict JSON Schema, fixing your retrieval logic, and enforcing atomic output structure.
You don't need another prompt engineer. You need an AI Architect.
I am an AI Systems Engineer and Physicist-Mathematician with production experience integrating LLMs (Gemini, GPT-4, LLaMA) into real-world SaaS platforms. My approach to AI is scientific: I don't "vibe-check" outputs — I run systematic evaluations using the RAG Triad (Context Relevance, Groundedness, Answer Relevance) measured with Python frameworks like TruLens against a golden dataset.
In the Starter tier, you receive a PDF report diagnosing your top 3 architecture issues and how to fix them. In the Standard tier, I audit your full pipeline: Vector DB strategy, chunking logic, prompt chain, and output parsing. In the Advanced tier, I deliver the report AND refactor the code — migrating fragile Markdown parsing to Strict JSON Schema, fixing your retrieval logic, and enforcing atomic output structure.
You don't need another prompt engineer. You need an AI Architect.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$300
|
Standard
$1,000
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
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 | - | - | - |
About Jonathan Elias
Data Entry & Transcription Services | Large Language Model
Morelia, Mexico - 6:55 am local time
> Knows Svelte, Svelte-kit + Tailwind.
> Auth, SQL, NoSQL, SEO, DigitalMarketing
Full project management from start to finish
Regular communication is important to me, so let’s keep in touch.
Steps for completing your project
After purchasing the project, send requirements so Jonathan Elias can start the project.
Delivery time starts when Jonathan Elias receives requirements from you.
Jonathan Elias works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1
Client submits requirements: current LLM stack, problem description, and access to architecture docs or a repo (if Advanced tier).
Step 2
I perform the architecture audit — stress-testing your pipeline with edge-case inputs, analyzing chunking strategy, evaluating retrieval precision, and running the RAG Triad evaluation suite.