You will get I will audit and optimize your RAG system so it stops hallucinating
Project details
Most RAG systems don't fail because of the model — they fail in retrieval, chunking, or evaluation. I audit the full pipeline, measure groundedness and retrieval precision, and fix what's actually breaking so your system cites sources and says "I don't know" instead of inventing answers.
What you get:
• A clear written audit with prioritized fixes
• Measurable quality baseline (and before/after on Standard/Advanced)
• Production-focused recommendations — not a chat demo
Packages: Starter ($200) audit + report · Standard ($550) audit + core fixes · Advanced ($1,200) optimization + reusable eval harness + handoff.
I ship production LLM/RAG systems with 90% Job Success on Upwork. If your RAG is unreliable, this is the fastest way to diagnose and fix it.
What you get:
• A clear written audit with prioritized fixes
• Measurable quality baseline (and before/after on Standard/Advanced)
• Production-focused recommendations — not a chat demo
Packages: Starter ($200) audit + report · Standard ($550) audit + core fixes · Advanced ($1,200) optimization + reusable eval harness + handoff.
I ship production LLM/RAG systems with 90% Job Success on Upwork. If your RAG is unreliable, this is the fastest way to diagnose and fix it.
AI Algorithms
Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Speech, AI-Generated Code, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
GitHub Copilot, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Replit, TensorFlowAI Models
ChatGPT, GPT-4, LaMDA, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$200
|
Standard
$550
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 10 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 |
6 reviews
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Mb
Michael b.
Jul 11, 2026
Phase 4 — confirmed: A + B + your 3 additions at $175 / 3 days
GW
Geordie W.
Jul 2, 2026
Personal AI Assistant Development with OpenClaw
Worked with Aiona on an openclaw fork that I didn't have time to look into. Plus, I wanted to test out her AI and dev skills for future projects. I am glad I did. She was easy to work with. organised and an excellent commitment to details and communication. A+ experience. Thank you!
AF
Alexander F.
May 21, 2026
Biological Credentials Registry Developer
Ainoa is just plain excellent. She is lightning fast, and talented, not to mention immediately picks up on concepts. She is worth probably twice her hourly rate. She keeps good track of work, follows instructions very well, avoiding drift and back work. She pivots well, and is attentive to detail. I will continue to use her for my project as much as possible.
Mb
Michael b.
Apr 29, 2026
**Small MoE Prompt Classifier Proof-of-Concept**
Rating: 5/5 Stars — Exceptional
Hired for a small proof-of-concept experiment that evolved into a three-phase research program spanning multiple model architectures and benchmark suites. Delivered at every stage with technical depth, intellectual honesty, and professional communication that I have rarely encountered from any contractor.
PHASE 1: Built a small Mixture-of-Experts model from five domain-specialized models, logged router weights, trained a classifier, and ran a bias injection experiment. Delivered a complete reproducible pipeline with professional report, heatmaps, and a dose-response curve — in roughly 24 hours. The bias sweep was not in the original scope; she added it because she recognized it would be the most informative result. She was right. That single addition became the foundational evidence for the entire research program.
PHASE 2: Adapted the framework to a production-scale MoE model (Mixtral 8x7B) to test quality improvement on real benchmarks. The result was negative — steering did not improve quality on this architecture. Most freelancers would have buried this or spun it. She reported it clearly, explained precisely why this architecture was resistant, cited relevant literature, and stated plainly: "the odds are against it, even on a better-suited model." That honesty prevented premature decisions and pointed directly to what the next experiment needed to test. She then voluntarily proposed three alternative research directions with detailed rationale and her honest recommendation for each.
PHASE 3: Adapted to DeepSeekMoE 16B (64 routed experts, specialization-optimized architecture) with a reframed primary metric — efficiency rather than accuracy. Identified five efficiency metrics capturable at zero additional implementation cost from existing infrastructure. Designed a two-stage profiling strategy to handle the 64-expert scale. Delivered a clean positive result: 42% entropy reduction, 35 percentage point weight concentration improvement, and accuracy improvement at the optimal operating point. Correctly identified the bounded nature of the sweet spot and documented the safe operating regime.
CROSS-PHASE ASSESSMENT:
Technical capability: Deep familiarity with MoE architectures (Mixtral, DeepSeekMoE, custom assemblies), routing mechanisms, benchmark design, statistical analysis, and Python infrastructure. Adapted fluently between three different architectures without hand-holding.
Intellectual honesty: Her most valuable trait. Reported negative results with the same precision as positive ones. Recommended against further spending when data didn't support it. Distinguished between what data showed and what it implied. Never overclaimed.
Communication: Proactive, clear, efficient. Anticipated questions before asked. Communicated infrastructure problems immediately with contingency plans ready. Turnaround consistently within hours.
Scope discipline: Consistently delivered above specification without inflating scope or timelines. Clearly distinguished between trivially cheap additions and meaningful additional work. Never padded.
Strategic thinking: This elevated her from excellent executor to research collaborator. She helped shape which experiments were worth running — identifying expert collapse patterns, recommending the right model for each phase, proposing the efficiency reframe, and providing three-option strategic proposals after negative results. These contributions influenced the research direction at every stage.
SUMMARY: Over three phases, she helped build a complete cross-architecture experimental program that validated a novel mechanism, documented a critical negative result that prevented premature filing, and demonstrated a positive result on the right architecture. Publication-quality reports, reproducible code, and raw data at every stage.
I am continuing to work with her on subsequent phases. Recommended without reservation for ML engineering work, particularly MoE architectures, model internals, experimental design, or research-grade analysis. The combination of technical excellence, intellectual honesty, proactive communication, and above-scope delivery in one freelancer is rare. I found it here and I intend to keep it.
Hired for a small proof-of-concept experiment that evolved into a three-phase research program spanning multiple model architectures and benchmark suites. Delivered at every stage with technical depth, intellectual honesty, and professional communication that I have rarely encountered from any contractor.
PHASE 1: Built a small Mixture-of-Experts model from five domain-specialized models, logged router weights, trained a classifier, and ran a bias injection experiment. Delivered a complete reproducible pipeline with professional report, heatmaps, and a dose-response curve — in roughly 24 hours. The bias sweep was not in the original scope; she added it because she recognized it would be the most informative result. She was right. That single addition became the foundational evidence for the entire research program.
PHASE 2: Adapted the framework to a production-scale MoE model (Mixtral 8x7B) to test quality improvement on real benchmarks. The result was negative — steering did not improve quality on this architecture. Most freelancers would have buried this or spun it. She reported it clearly, explained precisely why this architecture was resistant, cited relevant literature, and stated plainly: "the odds are against it, even on a better-suited model." That honesty prevented premature decisions and pointed directly to what the next experiment needed to test. She then voluntarily proposed three alternative research directions with detailed rationale and her honest recommendation for each.
PHASE 3: Adapted to DeepSeekMoE 16B (64 routed experts, specialization-optimized architecture) with a reframed primary metric — efficiency rather than accuracy. Identified five efficiency metrics capturable at zero additional implementation cost from existing infrastructure. Designed a two-stage profiling strategy to handle the 64-expert scale. Delivered a clean positive result: 42% entropy reduction, 35 percentage point weight concentration improvement, and accuracy improvement at the optimal operating point. Correctly identified the bounded nature of the sweet spot and documented the safe operating regime.
CROSS-PHASE ASSESSMENT:
Technical capability: Deep familiarity with MoE architectures (Mixtral, DeepSeekMoE, custom assemblies), routing mechanisms, benchmark design, statistical analysis, and Python infrastructure. Adapted fluently between three different architectures without hand-holding.
Intellectual honesty: Her most valuable trait. Reported negative results with the same precision as positive ones. Recommended against further spending when data didn't support it. Distinguished between what data showed and what it implied. Never overclaimed.
Communication: Proactive, clear, efficient. Anticipated questions before asked. Communicated infrastructure problems immediately with contingency plans ready. Turnaround consistently within hours.
Scope discipline: Consistently delivered above specification without inflating scope or timelines. Clearly distinguished between trivially cheap additions and meaningful additional work. Never padded.
Strategic thinking: This elevated her from excellent executor to research collaborator. She helped shape which experiments were worth running — identifying expert collapse patterns, recommending the right model for each phase, proposing the efficiency reframe, and providing three-option strategic proposals after negative results. These contributions influenced the research direction at every stage.
SUMMARY: Over three phases, she helped build a complete cross-architecture experimental program that validated a novel mechanism, documented a critical negative result that prevented premature filing, and demonstrated a positive result on the right architecture. Publication-quality reports, reproducible code, and raw data at every stage.
I am continuing to work with her on subsequent phases. Recommended without reservation for ML engineering work, particularly MoE architectures, model internals, experimental design, or research-grade analysis. The combination of technical excellence, intellectual honesty, proactive communication, and above-scope delivery in one freelancer is rare. I found it here and I intend to keep it.
JR
Juan R.
Mar 30, 2026
Expert Needed to Build RAG System LLM Pipeline
It was a pleasure working with Ainoa. She has a deep understanding of RAG and was able to implement a robust solution that met all our requirements. What I appreciated most was her clear communication and her ability to troubleshoot complex issues efficiently. The project was delivered on time and with high quality. Highly recommended for any technical AI implementation!
About Ainoa
AI Engineer | RAG, AI Agents & End-to-End LLM Apps (React + FastAPI)
100%
Job Success
Vigo de Galegos, Spain - 9:26 pm local time
WHO I WORK WITH
Teams and founders who tried a chatbot that hallucinated, wired up automation that broke without warning, or know AI should save them time but don't know what to build first.
WHAT I BUILD
• Grounded RAG & audits — retrieval that cites its sources and says "I don't know" instead of inventing answers. I'm often brought in to fix pipelines returning wrong or unreliable results.
• Multi-agent systems — agents that use tools, keep context, and hand off to humans, with approval gates, logging and cost control.
• End-to-end AI apps — FastAPI backends (streaming, auth, structured outputs, containerized deploys) plus the React frontend to use them: dashboards, chat UIs, internal tools.
WHY ME (the honest version)
I review the architecture before writing a line of code. If AI isn't the right tool for your problem, I'll tell you. My work lives at the layer where projects actually fail — retrieval accuracy, agent reliability, evaluation. 90% Job Success across 10 jobs, with clients who come back for follow-on phases.
RECENT WORK
• RAG system audit & optimization (retrieval + evaluation)
• Regulated-lab AI agent — human-in-the-loop + audit trail
• Record matching & email enrichment — confidence-based routing
Core stack: Python · FastAPI · LangChain/LangGraph · React (OpenAI / Claude).
Certified: Machine Learning Specialization (Stanford / Andrew Ng), LangChain for LLM App Development (DeepLearning.AI), NVIDIA Generative AI.
Send me one system or workflow that's stuck or eating your team's time. I'll reply with a short, honest teardown — what I'd build first, what I wouldn't, and what it'll take. No pitch.
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