LLM Prompt Engineering Jobs

RAG must be developed as an independent regulatory validation engine running after FINAL MERGE, using a closed-domain approach that operates only on uploaded official documents without external web search. It should run after final_merged_text is completed and Vision results are appended, connected from n8n only via a Side-Car API call. RAG must be deployed as a separate Docker container with a vector database in channel-specific namespaces already made in current workflow Input data should include final_merged_text and Vision tags, and RAG must not influence generation logic, only validate final outputs. The output must be a structured JSON validation report containing legal references, not just OK/NG. Because this is a closed-document RAG structure, it provides high accuracy and relativ...

AI Open-Source Model & System Integration ​We have an existing n8n-based AI video automation system. The task is to develop the features listed below and ensure seamless integration with the current system. UI designs provided. ​Difficulty: Low / Estimated Time: 3 hours ​Scope: Integrate voice and video generation AI open-source models (Wan2.1, LTV, VideoVibe, SVI, Qwen3, etc.) into the RTTM environment. Ensure connectivity with existing characters in the n8n system and modify UI/UX connections. ​[Mandatory Deliverable]: A Google Sheets-based manual including step-by-step screenshots, prompts, and configuration values (Video + Text). [Mandatory] tell me your portfolio related to this task. and Tell me price and timeline.

RAG Engine Construction & Data Training Integration ​We have an existing n8n-based AI video automation system. The task is to develop the features listed below and ensure seamless integration with the current system. UI designs provided. ​Difficulty: Low / Estimated Time: 4–5 hours ​Scope: Review the existing design of the Google-based RAG engine for large document training. Modify and connect data pipelines to ensure seamless integration with downstream n8n workflows and UI/UX connections. ​[Mandatory Deliverable]: A Google Sheets-based manual including step-by-step screenshots, prompts, and configuration values (Video + Text). [Mandatory] tell me your portfolio related to this task. and Tell me price and timeline.

I’m building a retrieval-augmented generation (RAG) pipeline and need a specialist to stand up the vector database layer for my large-language-model workflow. All content going into the store will be purely textual—think markdown files, knowledge-base articles, and long-form documents—so the schema, chunking strategy, and embedding approach should be optimised for fast, accurate text search. Here’s what I’d like from you: • Recommend and deploy a production-ready vector database (Pinecone, Weaviate, Chroma, Milvus or a comparable option). • Design a text-specific embedding and metadata schema, including parameters such as chunk size, overlap, and namespace strategy. • Build ingestion scripts that batch-process my existing documents, generate em...