Azure AI RAG Tutor & Problem-Solver Needed

@ivaylo8

Hi there, I’m Ivaylo, an Azure AI and RAG specialist who bridges the gap between complex AI concepts and practical, actionable fixes. I’ve helped teams diagnose and repair Retrieval-Augmented Generation pipelines built on Azure AI Foundry, Prompt Flow, and Azure AI Search, delivering clearer explanations and durable solutions, without jargon. What I’ll deliver in our diagnostic sessions: - A focused review of your RAG setup (Prompt Flow design, Python nodes, chunking strategy, embedding model, and search parameters) and a reproducible explanation of why the wrong documents are surfacing. - A step-by-step plan to fix the core issues, framed in simple terms: data alignment, embedding selection, chunk boundaries, and vector/hybrid search configurations. - Live, patient tutoring sessions (screen sharing as needed) to ensure you understand the root cause and the fixes, plus practical code changes you can apply directly in Prompt Flow if required. - Optional review of your Azure AI Search index structure (fields, metadata, and filterable attributes) to support more precise retrieval. Why this approach works: RAG behavior is driven by how data is chunked, embedded, and queried. A misalignment in any of these areas can create pull requests that don’t match user intent. I’ll translate the technical findings into actionable steps you can replicate and validate quickly. Looking forward to helping you build a robust, explainable RAG solution with confidence. Best regards, Ivaylo

@shakilan4

Hi, I can fix your RAG setup in Azure AI Foundry and explain the root cause clearly while improving retrieval accuracy. I’m experienced with Prompt Flow, Azure AI Search, and can guide you step-by-step in live sessions. Best regards, Shakila Naz

@asharib17

This sounds like a classic RAG misalignment issue—usually tied to chunking, embeddings, or how Azure AI Search is configured (vector vs hybrid + metadata filters). I’ve debugged similar cases where retrieval looked “random” but was actually a scoring/index design problem. I can help you not just fix it, but understand it clearly. My approach: * Review your Prompt Flow + retrieval pipeline end-to-end * Validate chunking strategy (size, overlap, semantic boundaries) * Check embedding consistency (model mismatch is a common issue) * Inspect Azure AI Search index (fields, vector config, filterable metadata) * Tune query strategy (top_k, hybrid search, filters) * Walk you through each fix in simple terms during live sessions Relevant experience: * Built and debugged RAG pipelines with vector + hybrid search (Azure/OpenAI stack) * Strong backend experience (Python, FastAPI, data pipelines) * Experience explaining complex systems to non-technical stakeholders Quick questions: 1. Are you using pure vector search or hybrid (BM25 + vector) in Azure AI Search? 2. What embedding model + chunk size are you currently using? Available for live screen-sharing sessions and can start with a focused diagnostic to pinpoint the issue quickly. If that works, let’s connect and review your setup together.

@AhmadXpert2410

Hi there, I understand you’re facing a classic RAG issue where Azure AI Search is returning irrelevant documents, and you want not just a fix but a clear understanding of the root cause. These problems typically stem from chunking strategy, embedding mismatches, index schema design, or search configuration (vector vs hybrid), so the goal is to systematically diagnose and correct the retrieval layer while making the logic easy for you to follow. I have experience building and debugging RAG pipelines, including working with embeddings, vector databases, and retrieval tuning (chunk size, overlap, metadata filtering, and query strategies). My approach will be to review your Prompt Flow step-by-step, inspect your Azure AI Search index (fields, scoring, filters), and test queries to identify where relevance breaks. I’ll then guide you live through fixing the issue—whether it’s improving chunking, refining embeddings, or adjusting hybrid search parameters—while explaining everything in simple, practical terms. I’m available for live screen-sharing sessions and can start with a diagnostic walkthrough to pinpoint the issue quickly. I’ve helped similar setups move from inconsistent retrieval to reliable, grounded responses, and I’ll ensure you leave with both a working solution and a solid understanding of how to maintain and improve it going forward. Regards, Ahmad

@sadam61

Hi there, I understand you're seeking expert help with your RAG application using Azure AI Foundry and Azure AI Search. I have solid experience in similar projects, where I successfully diagnosed and resolved retrieval issues by optimizing configurations and explaining the solutions clearly. My understanding of RAG architecture, chunking strategies, and embedding models allows me to get to the root of problems quickly. I can start immediately and would be happy to walk you through each step, ensuring you grasp the concepts. My availability for a first diagnostic session is flexible, and I can tailor sessions to fit your needs.

@danielboggs

Hi there, I’ve read your description and I can help diagnose why your Azure AI Foundry RAG pipeline is returning irrelevant documents and teach you how to fix it. I’ve worked directly with Prompt Flow, Python nodes, embedding choices, chunking strategies and Azure AI Search index design, and I focus on clear, patient explanations for non-experts. I will review your Prompt Flow setup, index schema, chunking and embedding configuration, reproduce the retrieval behavior, identify root causes (e.g., poor chunk boundaries, embedding mismatch, wrong search parameters or filters), and guide you step-by-step to correct them. I’ll also show how to test fixes and suggest durable configurations to avoid regressions. I’m happy to run a live diagnostic session and pair-program through Python nodes if needed. Could you share a short export or screenshots of your Prompt Flow (including Python nodes), the Azure AI Search index schema, and an example query with the retrieved documents that are wrong so I can reproduce the issue? Thanks, Daniel

@haseebsidd07

Hey , I just finished reading the job description and I see you are looking for someone experienced in Azure OpenAI, Retrieval-Augmented Generation (RAG), Microsoft Azure, Azure, Generative AI, Python and Consulting. This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Haseeb,

@daveg57

Hello, I have reviewed your project. While further detailed examination is necessary, your project is absolutely feasible in its current state, and I am very pleased to take on this task. First, the fundamental principle of your project is: Diagnose why your Azure AI Foundry RAG setup is returning irrelevant documents. We’ll review Prompt Flow, index configuration, chunking strategy, embedding model, and search parameters to identify the root cause. Then we’ll translate the findings into simple terms and practical fixes. I intend to write your project in the following manner: - Live diagnostic sessions to inspect Prompt Flow Python nodes, embedding settings, and chunking logic. - Step-by-step explanation of the root causes in plain language. - Implement fixes in a controlled manner and validate results with clear before/after comparisons. - Optional review of Azure AI Search index structure and metadata filterable fields as needed. I look forward to working with you and engaging in a detailed discussion.

@charlesm011

Hello, Thank you for the opportunity to submit this bid for your project. I have carefully reviewed the project’s scope and am excited to present my proposal. I’ve previously resolved RAG retrieval issues by tuning chunking, embeddings, and hybrid search, delivering consistently relevant results and clearer explanations for non-technical stakeholders. My plan: ✓ Review Prompt Flow, index config, chunking strategy, embeddings, and search parameters in Azure AI Foundry. ✓ Diagnose misalignment causes (noise, filtering gaps, or vector search issues) and explain them in plain terms. ✓ Implement fixes in Python nodes or config (adjust chunk size, reindex, fine-tune embeddings, refine filters) and validate with representative queries. ✓ Provide a concise playbook and live-session plan to ensure you understand and can maintain the setup. Would you prefer a diagnostic session approach focused on a live screen-share walkthrough, or a written step-by-step remediation plan? Best regards, Charles

@imranhaider2018

Hey , I just went through your job description and noticed you need someone skilled in Azure OpenAI, Consulting, Python, Retrieval-Augmented Generation (RAG), Microsoft Azure, Azure and Generative AI. That’s right up my alley. You can check my profile —I’m Software engineer working at large-scale apps as a lead developer with U.S. and European teams. I’ve handled several projects using these exact tools and technologies. Before we proceed, I’d like to clarify a few things: Are these all the project requirements or is there more to it? Do you already have any work done, or will this start from scratch? What’s your preferred deadline for completion? Why Work With Me? 1) Over 230 successful projects completed. 2) I have not received a single bad feedback since the last 5-6 years. 3) You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. 4) Long-term track record of happy clients and repeat work. I prioritize quality, deadlines, and clear communication. Availability: 9am – 9pm Eastern Time (Full-time freelancer) I can share recent examples of similar projects in chat. Let’s connect and discuss your vision in detail. Kind Regards, Imran Haider

@reierv

Hello, As an experienced AI developer with a particular focus in Microsoft's Azure AI suite, I'm thrilled at the opportunity to work on your project. I've spent substantial time on similar RAG projects which necessitated working with Azure AI Foundry and Azure AI Search. My grasp of RAG architecture, especially concepts like chunking, embeddings, vector search, and hybrid search matches your explicit demands. Pair that knowledge with my comprehensive understanding of the Prompt Flow framework, and you have an expert capable of delivering what you've asked for. However, what sets me apart from other candidates is my ability to explain technical information effectively to non-technical individuals. In our interactions, I'll take the time to understand your level of familiarity with each concept and tailor my explanations accordingly. When we troubleshoot the root causes in your setup or consider how to approach the Azure AI Search index structuring or even query routing strategies if necessary, I will make sure that we're not only solving the problem at hand but also enhancing your comprehension. Moreover, I appreciate and respect the value of clear communication in any consulting capacity. While you're likely seeking an efficient solution-based freelancer, proficiency alone isn't enough. You deserve a curious collaborator who will strive to understand your business to holistically implement effective solutions. So let's properly exa Thanks!

@benedyktl

Hello, As an esteemed professional with a proven track record on similar projects, I am confident in my ability to effectively diagnose and resolve the issues you're experiencing with your RAG application while also providing you with an enriching learning experience. My deep knowledge of Azure AI Foundry and Azure AI Search, as well as my extensive experience with Python and Prompt Flow nodes, makes me your ideal choice for this task. Plus, I have successfully collaborated with several clients for problem-solving and tutoring sessions. My career's unique blend of Full-Stack JavaScript development (MERN, MEAN, MEVN) and Artificial Intelligence affords me a broad perspective on digital ecosystems that I can now employ directly for the benefit of your project on Azure AI Foundry. Rest assured, not only can I detect the root cause behind relevant-document retrieval failures and solve them, but I would ensure you understand both the problem and its remedy in clear and concise terms. Let's get your RAG application functioning precisely as it should be! Finally, in addition to solving the immediate mishaps you are facing, I'm keen on optimizing your online presence for the long-term by ensuring that your Azure AI Search index is structured optimally while incorporating any specified filterable or searchable metadata fields as per best practices. When passion and expertise collide with strong collaborative skills like mine, incredible thi Thanks!

@robertr425

Hello, My name is Robert, and I'm an AI engineer with a strong focus on Azure AI Foundry and Azure AI Search. Throughout my 5+ years of experience, I have devoted much of my energy to developing sophisticated natural language processing (NLP) applications. One notable project involved the implementation of a RAG solution very similar to your own. My expertise helped that client pass from receiving inaccurate responses to correctly pulling meaningful documents that were relevant to their user queries. Beyond my technical prowess, my true differentiator is my teaching ability. I understand the importance of clear communication when working with complex technologies, particularly if you're not an expert yourself. Guiding and educating clients during the process is a natural part of what I do, so I am ready to patiently explain all the intricacies of RAG architecture to ensure you are able to comprehend and apply them effectively. As for your project, my skills map perfectly onto your needs: I know Prompt Flow inside-out, have extensive experience with Python nodes, understand how hybrid search works and have prior expertise configuring filterable metadata fields in Azure AI Search. On top of this, by choosing me for your project, you'll gain access to my proprietary frameworks and AI-assisted coding practices that ensure 30% faster development and optimized load times - something that would be quite handy during our debugging sessions! Thanks!

@mihail81

Hello, As an experienced AI-powered Full Stack Developer with a focus on Azure AI Foundry and Azure AI Search, I have not only the technical prowess but also the ability to translate complex concepts into plain language essential for this project. In a similar RAG project, I used my deep understanding of chunking, embeddings, vector search, and hybrid search to not only identify an issue similar to yours but also explain it effectively to non-experts. I believe this makes me best-suited for your project as you seek clarity and guidance alongside the necessary problem-solving. In terms of skillset, I have wide-ranging expertise in Prompt Flow, Python nodes, flow design, metadata configuration in Azure AI Search and more. This uniquely positions me to meticulously inspect your current RAG setup and pinpoint the discrepancies leading to the retrieval of incorrect documents. Furthermore, my experience in both traditional development and cutting-edge AI will ensure that your solution is not just fixed, but future-proofed throughout. Drawing from my consulting background, I am adept at fostering informative sessions through screen sharing. This way, we can collaboratively delve into the issues at hand while educating and reinforcing your understanding of potential remedies. My hourly rate for this scope is competitive and reflective of my dedication to deliver robust solutions within budgetary constraints. I welcome a first diagnostic s Thanks!

@shymaa78

Hello there, I see you're looking for expertise in your RAG application using Azure AI Foundry, specifically to tackle issues with document retrieval. With 5+ years of experience in this space, I can help diagnose why your application is returning incorrect documents and explain the problem in a straightforward way. My approach would involve reviewing your current setup, including the index configuration and chunking strategy, to pinpoint the root cause and guide you through implementing the right fixes. I've worked on similar projects where I’ve successfully optimized retrieval processes and improved user query responses. I’m also comfortable using Python nodes in Prompt Flow and can ensure that the technical concepts are easy to grasp. Looking forward to helping you enhance your application! Best regards, shyimaa

@peterj514

Hi there, I’ll tackle your Azure AI Foundry RAG challenge head-on by diagnosing why your retrieval returns irrelevant docs and translating the fix into simple, actionable steps. I’ve spent the last 4 years solving exactly this kind of misalignment between embeddings, chunking, and search parameters in RAG pipelines. I’ll review Prompt Flow Python nodes, chunking strategy, embedding model, and Azure AI Search index settings; then simulate queries to pinpoint if the issue is semantic drift, poor vector similarity, or misconfigured filters. I’ll explain the root cause in plain terms and guide you through corrective config changes and, if needed, Python node tweaks in Prompt Flow. Finally, I’ll propose a robust validation plan so you can verify improvements across sessions. Best regards,

@BlocKeyFi

Hi, I understand that you are seeking a knowledgeable Azure AI expert to assist with your RAG application in Azure AI Foundry. I am experienced in Azure AI Foundry and Azure AI Search, and I am confident in my ability to diagnose and resolve the issues you are facing with document retrieval. I specialize in explaining technical concepts clearly and guiding non-experts through solutions effectively. In this project, I will review your current RAG setup, diagnose retrieval issues, provide clear explanations, and assist in fixing any configuration or code problems. I have a strong background in RAG architecture, Prompt Flow, and live session collaboration, which will be beneficial for our work together. I will share my portfolio via direct message for you to review. Feel free to reach out there. My background in Azure AI technologies ensures reliable results, professional standards, and seamless execution. I'm available to discuss your project requirements further and address any concerns. Best regards, Taneem

@ayesha86664

Hi there! You’re building a RAG system in Azure AI Foundry, and the real challenge is ensuring the retrieval step consistently returns relevant documents — that is exactly where most setups struggle, especially with chunking and vector search configuration. I’ve worked on RAG pipelines that combine Prompt Flow with Azure AI Search, optimizing embeddings, chunk sizes, and metadata filtering to drastically improve retrieval accuracy. I will review your current configuration, explain the root cause of irrelevant results in plain language, and guide you step-by-step to a corrected setup that you can replicate confidently. Check our work: https://www.freelancer.com/u/ayesha86664 Do you want the solution to focus purely on vector search improvements, or should we also adjust hybrid search and metadata filters for maximum relevance? I am ready to start — just say the word. Best Regards, Ayesha

@melaneb1

----------------------- ✅✅✅✅✅ Ready To Support You Fully ✅✅✅✅✅ ----------------------- Your issue is a classic RAG challenge — retrieval returning irrelevant documents usually comes down to **chunking strategy, embedding mismatch, or search configuration**, and I can help you not only fix it, but truly understand *why it’s happening*. I’ve worked on multiple RAG systems using **Azure AI Foundry + Azure AI Search**, including a document intelligence assistant where incorrect retrieval was caused by poor chunk boundaries and missing metadata filters. After restructuring the index and switching to hybrid search (vector + keyword), retrieval accuracy improved significantly. For your project, my approach will be: • **Step 1: Diagnose Clearly** – Review your Prompt Flow, embeddings, index schema, and query settings to pinpoint the exact failure (not guessing — precise root cause). • **Step 2: Explain Simply** – I’ll walk you through the issue in plain language so you fully understand what’s wrong and why. • **Step 3: Fix Together** – Improve chunking, embedding usage, and search parameters (k, filters, semantic config, hybrid search). • **Step 4: Optimize** – Ensure your system consistently retrieves relevant documents with scalable logic. **Availability:** Can start immediately, flexible for live sessions (screen sharing). **Rate:** $20 hourly Let’s fix your RAG pipeline *the right way* — and make sure you walk away confident using it.