Hybrid RAG System Enhancement Project

$10-30 USD

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$10-30 USD

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Project Proposal Title:Enhancement of a Hybrid Retrieval-Augmented Generation System with Keyword and Semantic Search, Reward-Shaped Retrieval, and ReComp-Based Context Compression. [keyvalue]([login to view URL]) This project proposes a hybrid RAG architecture that combines keyword-based retrieval with semantic search, adds a reward-and-punishment scheme to improve retrieval relevance, and uses ReComp-style context compression to handle long contexts more efficiently. [arxiv]([login to view URL]) Background Retrieval-Augmented Generation is widely used to reduce LLM limitations such as knowledge cut-off, hallucinations, and lack of transparency, because it grounds generation in retrieved external evidence. Hybrid search is especially useful because keyword retrieval improves exact-match precision while semantic retrieval helps recover conceptually related content. [toloka]([login to view URL]) Context compression methods such as ReComp-like approaches are important because they reduce prompt size while trying to preserve faithful information from retrieved documents. Recent work also shows that retrieval systems can benefit from reward-based reasoning and reflection mechanisms that explicitly encourage sufficient and accurate evidence use. [arxiv]([login to view URL]) Problem Statement Current RAG systems often retrieve partially relevant or unrelated passages, and this harms answer quality even when the generation model is strong. The challenge is not only to increase recall, but also to score retrieved items so that related content is rewarded and unrelated content is penalized, especially in the semantic retrieval stage. [superlinked]([login to view URL]) Objectives - Build a hybrid retriever that combines keyword search and semantic search into one ranking pipeline. [keyvalue]([login to view URL]) - Design a semantic relevance scoring mechanism that rewards closely related passages and penalizes unrelated passages. [arxiv]([login to view URL]) - Add ReComp-based long-context compression so that retrieved evidence can be condensed without losing faithfulness. [iclr]([login to view URL]) - Evaluate the system with a target of above 90% accuracy on general context tasks. [arxiv]([login to view URL]) - Test performance separately across architecture, law, medicine, finance, and computer-related domains, and record the results by domain. [arxiv]([login to view URL]) Proposed Method Retrieval Layer The retriever will use both sparse keyword matching and dense semantic embedding search, then merge results using a hybrid fusion strategy such as reciprocal-rank-style ranking or reranking. This design helps preserve exact term matching while also capturing conceptually similar documents. [superlinked]([login to view URL]) Semantic Reward System A scoring module will measure retrieval usefulness using relevance labels or weak supervision, then assign positive reward to related content and negative reward to irrelevant content. This can be implemented with similarity thresholds, pairwise ranking loss, or reinforcement-style feedback so the retriever learns to prefer evidence that actually supports the query. [arxiv]([login to view URL]) Compression Layer After retrieval, a ReComp-inspired compression module will shorten long evidence contexts while preserving critical facts for generation. This will help keep prompts concise, reduce latency, and improve faithful generation under long-document conditions. [neurips]([login to view URL]) Generation Layer The generator will answer only from the compressed retrieved evidence, with citation-style grounding if required by the application. This should improve transparency and reduce hallucinations compared with unguided generation. [toloka]([login to view URL]) Evaluation Plan The system will be evaluated on two levels: retrieval quality and answer quality. Retrieval will be measured with relevance-oriented metrics such as precision, recall, MRR, and nDCG, while generation will be measured with exact match, F1, faithfulness, and human judgment for groundedness. [arxiv]([login to view URL]) A domain-wise benchmark will be created for: - Architecture. - Law. - Medicine. - Finance. - Computer-related topics. [arxiv]([login to view URL]) For each domain, results will be reported separately so the system’s strengths and weaknesses are visible across fields. The main success criterion is to reach above 90% accuracy on general context tasks, while also maintaining strong domain-specific performance. [arxiv]([login to view URL]) Expected Outcomes The proposed system is expected to improve retrieval relevance, reduce unrelated passage selection, and produce more faithful answers under long-context settings. It should also provide a clearer performance profile across domains, which is important because RAG evaluation is highly domain-dependent. [arxiv]([login to view URL])

AI Model Development

Reinforcement Learning

AI Research

Natural Language Processing

Machine Learning (ML)

Project ID: 40343490

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12 freelancers are bidding on average $28 USD for this job

@avinashdubeyg209

Noticed your focus on a reward-and-punishment scheme to boost retrieval relevance. Recently enhanced a fintech RAG pipeline using similar reward-based techniques, resulting in a 20% retrieval efficiency improvement. Quick question: how are you currently handling trade-offs between keyword accuracy and semantic depth? This could impact context compression using ReComp. Let's discuss this further, can dive into a quick plan to refine these aspects, ensuring that your hybrid RAG system's performance and scalability are not only maintained but enhanced.

$10 USD in 3 days

5.0

(33 reviews)

5.0

@Shonali1406

Hello, I bring 8+ years of experience in Machine Learning, Natural Language Processing, AI Model Development, AI Research, and Reinforcement Learning, delivering advanced AI solutions for complex information retrieval and generation tasks. My expertise includes: Building hybrid retrieval systems combining keyword and semantic search Implementing reward-shaped relevance scoring to optimize retrieval quality Integrating ReComp-style context compression for long-document handling Evaluating systems with precision, recall, MRR, nDCG, F1, and domain-specific benchmarks Why hire me: I combine deep technical proficiency with practical experience in research-grade AI, ensuring the prototype meets high accuracy and domain-specific requirements. I prioritize clear communication and look forward to collaborating closely to deliver a fully functional, research-ready system. Best regards

$25 USD in 7 days

5.0

(1 review)

4.3

@webmasters486

Hi there, I’ve carefully reviewed the requirements for your GenAI project and I’m confident that my expertise in building NLP pipelines using Hugging Face and LangChain can meet your expectations. My experience includes working with large language models (LLMs) for Retrieval-Augmented Generation (RAG), as well as fine-tuning models with custom datasets to enhance text generation. I’ve successfully completed similar projects where I applied these techniques in Python to build robust, client-specific solutions. I would love the opportunity to discuss how I can leverage my skills to develop a tailored solution for your project. Feel free to take a look at my portfolio to get a sense of the work I’ve done: Portfolio: https://www.freelancer.com/u/webmasters486/AI-automation Looking forward to hearing from you! Best regards, Muhammad Adil

$30 USD in 1 day

5.0

(3 reviews)

3.4

@jideo6

Hello Mate!Greetings , Good morning! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Machine Learning (ML), Reinforcement Learning, AI Research, Natural Language Processing and AI Model Development. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Have a great time

$15 USD in 1 day

0.0

(0 reviews)

0.0

@indreshpnk

I'd like to express my enthusiasm for your Hybrid RAG System Enhancement Project. As a seasoned Full-stack Cloud Developer with AWS certification and a deep understanding of AI/ML, I am adept at architecting robust and scalable systems that can intelligently incorporate the specified keyword and semantic retrieval upgrades. My extensive experience in NLP and ML models such as TensorFlow and PyTorch will enable me to design and implement an effective semantic relevance scoring mechanism for the retriever, ensuring that closely related passages are rewarded while unrelated ones are penalized. My expertise in ReComp-based context compression aligns perfectly with your need to effectively condense retrieved evidences without sacrificing critical information.

$100 USD in 7 days

0.0

(0 reviews)

0.0

@Mudassar295

Hello, I can design and implement your Hybrid Retrieval-Augmented Generation (RAG) system with a strong focus on accuracy, efficiency, and production readiness. My approach combines keyword-based retrieval (BM25) with semantic vector search to ensure both exact matches and concept-level understanding. I will integrate a hybrid ranking pipeline using techniques like reciprocal rank fusion and reranking to improve retrieval precision. To address irrelevant context issues, I will build a reward-based scoring mechanism that promotes highly relevant passages and penalizes weak matches using similarity thresholds and ranking optimization. For long-context challenges, I will implement a ReComp-inspired compression layer to reduce token usage while preserving critical information for grounded responses. The system will be deployed using FastAPI with modular, scalable components. I will also include a robust evaluation framework using metrics like Precision, Recall, MRR, nDCG, and answer-level F1/faithfulness. Domain-specific testing (law, medicine, finance, etc.) will ensure consistent performance. With my experience in RAG systems, LangGraph, and production AI pipelines, I can deliver a high-quality, optimized solution tailored to your needs. Let’s discuss your dataset and deployment preferences. Best Regards, Mudassar Niaz

$20 USD in 2 days

0.0

(0 reviews)

0.0

@ABEYSINGHEGTR

With a unique blend of skills in AI, machine learning, and electronics, I am the ideal candidate for enhancing your hybrid RAG system.

$25 USD in 6 days

0.0

(0 reviews)

0.0

@tulasigdp

This project aligns closely with the work I've done in RAG systems. Let me breakdown exactly how I'd approach each layer: Retrieval Layer: I'll implement hybrid search combining BM25/sparse keyword matching with dense semantic embeddings, fused via reciprocal rank fusion - This preserves exact match precision while recovering semantically similar passages. Semantic Reward System: I'll design a relevance scoring module using similarity thresholds and pairwise ranking loss to reward related passages and penalise unrelated ones - directly addressing the core problem. Compression Layer: I'll implement a ReComp-inspired module that condenses long retrieved contexts while preserving the facts needed for faithful generation, reducing prompt size and latency. Evaluation: I'll run domain-wise benchmarks across architecture, law, medicine, finance and CS - reporting results separately so you get a clear performance profile per domain

$25 USD in 7 days