RAG overcomes the limitations of language models by connecting them to a dynamic document base. This approach guarantees answers that are more reliable, traceable and adapted to the business context.
Create semantic databases with FAISS, Weaviate, Qdrant or Pinecone for fast, relevant searches.
Vector representation generation via OpenAI, Hugging Face, Cohere or SentenceTransformers, depending on domain and volume.
Technical chain: retrieval → reranking → prompt injection → generation, orchestrated to maximize relevance.
Intelligent chunking, sliding window, memory window management for efficient document coverage.
Measurements of groundedness, hallucination rate, relevance score to guarantee the quality of responses generated.
Creation of intelligent architectures, APIs, conversational agents, recommendation systems
Integration of models such as GPT, LLaMA, Mistral, Claude, etc. into business workflows
Adaptation of pre-trained models to specific corpora, supervised or reinforcement training
Design and training of deep learning models (CNN, RNN, Transformers) & machine learning (Random Forest, Scikit-Learn) for complex cases
Combining documentary research and generation for precise, contextualized answers
Design and deployment of modular, secure local AI architectures capable of processing data directly on the device
Containerization, CI/CD, monitoring, scalability, model security in production
Consultant specializing in the development of solutions based on theartificial intelligencethe language models (LLM) and neural networks.
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