Reduce AI Hallucination Errors

@snbhanja

Hi, I have worked and deployed many custom RAG/agentic ai solutions in cloud GPU environments. I can debug and optimise each step of your RAG process to reduce hallucination. While you already mentioned two metrics like factual consistency, BERTScore, but there are other metrics also, which I will use for evaluation. The search method will be also optimised along with vector DB coltion parameters. Please let me know.

@chraheelbhatti

As a PhD Researcher with a decade's experience as a Senior Machine Learning Engineer, I have tackled complex AI challenges in my work across various domains. My expertise spans Text-based models and Multimodal models like the one you're working on, enabling me to comprehensively audit and optimize your system. In your project, I identify the imperative need for accurate responses, rooted in fact, which aligns perfectly with my routine involvement in refining NLP systems to reducing errors

@ahmadraza001912

As an experienced full-stack developer and app engineer, I can confidently say that I'm the expert you need to reduce AI hallucination errors in your NLP-driven multimodal assistant. My core expertise in working with tools like PyTorch, LangChain, vector databases, and multimodal embeddings aligns perfectly with the requirements of your project. Over my 4+ years of hands-on experience, I've developed a strong grasp on backend APIs and databases (including PostgreSQL), cloud infrastructure handling (especially AWS GPU instances), and performance optimization. This ensures that my solutions not only deliver measurable improvements but also keep latency below your 10% threshold. To further solidify my pitch, my work is known for clean architecture, accuracy, quick turnarounds, and minimal disruption to live systems. Rest assured, you'll have clean, reproducible code/notebooks configured for your AWS GPU instance along with comprehensive documentation after completion. I believe a strong understanding of requirements beforehand saves time and guarantees better outcomes. Let’s take your NLP model to new heights together!

@haneenm57

1. Executive Summary I will transition your RAG pipeline to a Self-Corrective Agentic Workflow, ensuring ≥90% accuracy with <10% latency overhead. 2. Technical Methodology Tier 1: Intelligent Retrieval: Implementing Hybrid Search (Vector + BM25) and a Cross-Encoder Re-ranker to eliminate "context noise" and prioritize technical precision. Tier 2: Verification: Utilizing Corrective RAG (CRAG) for automated document scoring and Chain-of-Verification (CoVe) to cross-check claims against retrieved facts before output. Tier 3: Optimization: Deploying FP16/INT8 Quantization and Flash Attention on AWS GPU to maintain speed without compromising reasoning depth. 3. Evaluation & Deliverables I will provide a Performance Audit Report using RAGAS and BERTScore to validate Faithfulness and Relevance. Codebase: Clean, modular Python (LangChain/PyTorch) + Docker. Automation: A "One-Command" script for re-indexing/fine-tuning. Benchmarking: Proven 90% accuracy on your 500-item test set. 4. Timeline Phase 1: Error analysis & Baseline (3 days). Phase 2: Hybrid Retrieval & CRAG implementation (7 days). Phase 3: AWS Optimization & Final Audit (4 days). My background in data analysis ensures a focus on Data Integrity. Ready to begin immediately.

@guptakishan805

Hello, I can audit and optimize your RAG pipeline to significantly reduce hallucinations and improve factual accuracy. Approach: • Diagnose failure points (retrieval gaps, prompt leakage, weak grounding) • Improve retrieval with hybrid search (BM25 + embeddings) + re-ranking • Add strict grounding prompts with citation enforcement • Implement guardrails (confidence scoring, fallback responses) • Fine-tune or instruction-tune model with factual datasets Enhancements: • Context filtering + chunk optimization • Multi-step verification (self-check / cross-check prompts) • Lightweight eval pipeline (BERTScore + factual consistency) I’ll deliver a reproducible pipeline, tested on your dataset with ≥90% accuracy and minimal latency impact. Experienced with Hugging Face, LangChain, and multimodal systems—ready to start immediately.

@Elsenosy

Hello, I can help reduce hallucinations in your multimodal RAG system by improving the pipeline end-to-end rather than applying isolated fixes. My approach focuses on three key areas: 1- Retrieval optimization: better chunking, hybrid search (dense + BM25), and re-ranking to ensure only highly relevant context is used. 2- Grounded generation: prompt redesign, strict context-based answering, and citation enforcement to prevent unsupported outputs. 3- Validation layer: lightweight verification and guardrails to catch and filter hallucinations before responses are returned. I will deliver an improved pipeline, evaluation report (factual accuracy, BERTScore, and human review), and clean reproducible code ready for AWS. The target is ≥90% accuracy on your benchmark with minimal latency impact. Happy to start with a quick audit of your current outputs to identify the main failure points. Best regards, Ahmed

@prathamr34

Hey mate Hope you are doing well. I am pratham from NIT JAMSHEDPUR 3rd year. I am data scientist enthusiast.I know how nlp ,llm and transformer work. I have also made project on RAG but in multimodel .I can learn,try and help you out from this .if you want to see my last works on the project it will be very grateful for me to send my RESUME. I hope you count me as fresher student as a client .

@AsifTech21

As your search is focused on mitigating the AI hallucination errors affecting your multimodal assistant, I believe my extensive AI development background could be a valuable asset to your project. Having built similar advanced systems such as AI Email Responders that reduced manual workload by 80% and RAG-based document search engines, I already understand the importance of staying firmly grounded in fact. Firstly, an improved model that passes your benchmark with flying colours - I consistently aim for above 90% accuracy but not at the expense of increased latency which I promise will be kept below 10%. Secondly, you’ll receive a comprehensive evaluation report supported by automatic metrics such as factual consistency and BERTScore supplemented by human reviews. Lastly, my code deliverables will be clean, easily reproducible for AWS GPUs and come paired with clear documentation for a facile retrain. By leveraging your existing core pipeline with LangChain, Python, vector databases and multimodal embeddings alongside my adeptness with automation tools and APIs, I'm incredibly confident I can provide you with the impact-driven results you seek. Let's connect and take this crucial step towards eliminating critical factual errors together!

@omarn30

I will audit your multimodal RAG pipeline end-to-end to identify the root causes of hallucinations, whether they stem from retrieval gaps, weak grounding, or generation behavior. Based on this analysis, I’ll implement a targeted mitigation strategy to improve factual accuracy. This includes enhancing retrieval through better chunking, query rewriting, and re-ranking to ensure high-quality context, along with prompt engineering techniques that enforce grounded, evidence-based responses. I will also introduce guardrails such as answer validation, confidence scoring, and fallback mechanisms to reduce unsupported outputs. Where beneficial, I’ll apply focused data augmentation and lightweight fine-tuning to improve factual consistency, and optimize decoding parameters to further limit hallucinations without increasing latency significantly. The final deliverable will be a refined, reproducible pipeline ready for your AWS GPU setup, along with clear documentation for one-command retraining. I will also provide a detailed evaluation report using metrics like factual consistency and BERTScore, plus a small human-reviewed sample. My goal is to achieve ≥90% accuracy on your benchmark while keeping latency within the required limits.

@karthikreddyk

Hi there, I am a Machine Learning Developer specializing in multimodal LLM orchestration and RAG optimization. Hallucination in multimodal systems usually stems from either "retrieval noise" or "knowledge cutoff drift," and I have the specific experience to solve this for your pipeline. My approach to reaching your ≥90% accuracy target involves: Self-Correction & Verification Loops: I will implement a "Chain of Verification" (CoVe) or a "Self-RAG" architecture. The system will be programmed to cite its sources and cross-check the generated response against the retrieved context before final output. Multimodal Alignment: Drawing from my recent work in multimodal detection systems (specifically text-and-image processing), I will audit your embedding space to ensure image and audio inputs are correctly aligned with your vector database's text tokens to prevent "drift" during cross-modal retrieval. Factuality Benchmarking: I will use frameworks like RAGAS or TruLens to provide measurable scores for "Faithfulness" and "Answer Relevance," giving you the 500-item benchmark report you require. AWS Deployment: I am proficient with PyTorch and AWS GPU instances (EC2/Sagemaker), ensuring the revised pipeline is clean, containerized, and ready for one-command execution. I focus on high-efficiency, logic-based AI systems and can start the audit of your Hugging Face pipeline immediately. Best regards, Karthik Reddy

@deepikaAI

Hi, Your setup (Transformer + RAG) is solid—hallucinations usually come from retrieval gaps or weak grounding. I can audit your full pipeline and implement targeted fixes to improve factual accuracy while keeping latency low. **Approach** * Diagnose where errors occur (retrieval, context use, or generation drift) * Improve retrieval (better embeddings, hybrid search, reranking) * Add grounded prompting (strict context use, citations, refusal if unsure) * Introduce lightweight validation to catch incorrect outputs * Fine-tune with factual/contrastive data if needed * Align multimodal inputs (text, image, audio) for consistent grounding **Deliverables** * Improved pipeline with measurable accuracy gains * Evaluation report (accuracy, BERTScore + sample human review) * Clean, reproducible AWS-ready code **Targets** * ≥90% accuracy on your test set * <10% latency increase * Zero critical hallucinations via guardrails **Timeline: 3 weeks** (Week 1 audit → Week 2 improvements → Week 3 evaluation & delivery) Happy to start with a quick diagnostic on your dataset. Best, Deepika Dubey

@vishalswami007

Hi, Your issue likely comes from a mix of retrieval gaps, weak grounding, and multimodal misalignment. I can audit and optimize your full RAG pipeline to significantly reduce hallucinations while keeping latency low. Approach Diagnose failures (retrieval vs generation vs multimodal) Improve retrieval (better chunking, hybrid search, re-ranking) Enforce strict grounding via prompt engineering Add verification layer (self-check + guardrails) Optimize multimodal alignment (image/audio → text consistency) Optional fine-tuning with truth-focused data Tech PyTorch, Hugging Face Transformers, LangChain Vector DBs + AWS deployment Deliverables Improved pipeline (≥90% accuracy target) Evaluation report (metrics + sample review) Reproducible code + one-command retraining Timeline ~10–14 days I can start by analyzing a few failing cases to quickly identify root issues.

@DanielRoss3

Low price, high output. You get clean code, fast delivery, and someone who actually understands what they're building. Best value for your budget. That's it.

@mrbhautik

Your RAG pipeline is retrieving context but your LLM isn't staying grounded in it. That's where the hallucinations are coming from. I've built exactly this kind of system and I know where to look. I've previously built a Multi-Document Chat System using LangChain + vector embeddings where hallucination control was critical users needed accurate answers strictly from uploaded documents, not model imagination. Here's my exact approach for your pipeline: - Audit first: Review your chunking strategy, embedding model, retrieval logic, and prompt structure to pinpoint where hallucinations enter - Fix retrieval: Improve semantic chunking + add a CrossEncoder reranker to filter bad context before it hits the LLM - Strict prompt grounding: Enforce "answer only from retrieved context, say I don't know if uncertain" with chain-of-thought prompting - Multimodal alignment: Ensure your image and audio (Whisper) context is properly fused with text before retrieval quick question: What embedding model are you currently using for retrieval is it the same Hugging Face model or a separate one like text-embedding-ada-002? I'll do a free audit of your current pipeline and share my diagnosis with specific fixes before you award the project. You'll see exactly what's wrong and how I'll fix it, with zero risk. Since I'm building my profile on Freelancer.com, I'm offering a competitive rate for work.