Applied Machine Learning, Mathematics, Software Engineering, and Real World Solutions

RAG Pipelines in Practice

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Unlocking Smarter AI with Retrieval-Augmented Generation (RAG)

In the world of AI, even the most advanced generative models can fall short when asked about niche topics, specialized datasets, or emerging fields. Retrieval-Augmented Generation (RAG) bridges this gap by combining two powerful paradigms:

🔍 Retrieval: Rapidly fetch relevant information—text, images, time-series data, or audio embeddings—from external indexes.

🧠 Generation: Seamlessly integrate that retrieved information into context-rich, fluent responses using large language models (LLMs) or other generative systems.

Together, these capabilities form a synergistic pipeline that dramatically boosts accuracy, relevance, and reasoning—especially across multimodal data like images, audio, text, and time series.

🧩 Anatomy of a RAG Pipeline

A well-designed RAG pipeline typically includes four stages:

1. Data Ingestion & Indexing

  • Extract embeddings from documents, images, audio, or time series using specialized encoders (e.g., CLIP for vision, Transformers for text/audio/time series).
  • Store those embeddings in a vector database (e.g., FAISS, OpenSearch, HNSW).

2. Query Encoding & Retrieval

  • Convert user input into the same embedding space.
  • Run a nearest-neighbor search (KNN) to fetch the top-k relevant results.

3. Context Assembly

  • Aggregate the retrieved chunks (text passages, image metadata, sensor data) into a coherent context window.
  • Optionally re-rank, filter, or validate the context to reduce hallucinations.

4. Generative Synthesis

  • Feed the final context into a generative model (e.g., GPT, multimodal decoder).
  • Output a grounded answer, summary, or multimodal insight that cites retrieved data.

🔀 Multimodal RAG in Action

Vision + Text: CLIP-Powered Image Search

  • Inference Endpoint: Deployed a CLIP patch32 model on SageMaker, automating infra setup with Python SDKs and S3.
  • Vector Indexing: Stored image and text embeddings in OpenSearch for fast KNN retrieval.
  • Outcome: Achieved a 6% accuracy boost on CIFAR-10 after fine-tuning with contrastive loss and multimodal data augmentation.

Time Series: Retrieval + Forecasting

  • Trained a compact Transformer on the GiftEval dataset to convert time-series patterns into embeddings.
  • Indexed those using HNSW for lightning-fast similarity search.
  • Built a retrieval pipeline to fetch historical analogs, aiding both anomaly detection and forecasting.

Audio Signal Interpretation

  • Extracted spectrogram features (centroid, bandwidth, rolloff).
  • Indexed features for unsupervised clustering.
  • Achieved 95% accuracy on unlabeled audio using K-means, validating strong embedding quality for multimodal use.

🏢 Enterprise RAG: Prototype at Accenture Federal Services (AFS)

As an Associate Manager in AFS’s Machine Learning Group, Mridul Sarkar led the development of an enterprise-grade generative AI service designed for government workflows:

  • Prototyped RAG pipelines using Elasticsearch VectorDB for KNN retrieval of intelligence briefs.
  • Served LLMs via FastAPI microservices with integrated testing.
  • Drafted legal/security/implementation whitepaper on LLM usage and data governance for federal adoption.

✅ Best Practices for Building RAG Systems

Choose the Right Embeddings

  • Vision: CLIP, ViT, ResNet (domain-tuned)
  • Text/Audio: BERT, Wav2Vec, HuBERT
  • Time Series: Temporal Convolutional Nets, TS-BERT

Optimize the Vector Store

  • Use HNSW or IVF+PQ for scalability.
  • Tune index parameters to balance recall and latency.

Context Management

  • Dynamically vary top-k retrieval based on query complexity.
  • Use cross-encoders or scoring models to re-rank results.

Guard Against Hallucinations

  • Include source snippets in output.
  • Apply confidence thresholds or retrieval overlap checks.

Iterate and Monitor

  • Continuously fine-tune embedding encoders on evolving datasets.
  • Monitor retrieval accuracy, generation quality, and user feedback.

🔭 Looking Ahead: Generalized AI through RAG

Multimodal RAG pipelines mark a turning point in building more grounded, intelligent, and adaptable AI systems. By anchoring generation in verifiable knowledge, they unlock use cases ranging from interpreting satellite signals to curating research fellowships or debugging quantum circuits.

With experience spanning from quantum tensor network optimization at BlueQubit’s Hackathon to enterprise-grade AWS-based AI architecture, Mridul Sarkar exemplifies what it takes to bring RAG from theory to impact: accuracy, speed, and scalable design.

💡 Ready to Build Your Own RAG System?

Whether you’re looking to integrate RAG into your product, explore hands-on workshops, or launch a full-stack prototype—reach out and let’s build the future of grounded intelligence together.

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