GenAI RAG Prototyping
Development and validation of a Retrieval-Augmented Generation (RAG) prototype using AWS to assess the feasibility of leveraging internal technical documentation for AI-powered question answering.
Semiconductor Client
2025
Client Description
The client is a global semiconductor leader renowned for delivering high-performance, energy-efficient technology solutions across automotive, industrial, and consumer markets.
Problem
Fragmented Documentation Hindering AI Application
The client aimed to enable internal engineers and technical staff to query large volumes of technical documentation using natural language – for example, to understand design decisions, component specifications, or system limitations. However, there was no existing infrastructure or tooling in place to support such a solution. The available documents had not been validated for their suitability in a generative AI context, and it remained unclear whether a Retrieval-Augmented Generation (RAG) approach would yield accurate and reliable results. Additionally, the client lacked a method for prototyping or experimenting with such technologies in a controlled, secure environment.
Solution
Data Assessment and Prototyping of a RAG-Based Architecture on AWS
A structured data exploration phase was conducted to systematically validate the technical documentation's relevance, structure, and quality with respect to RAG requirements. Key insights were derived to define architectural boundaries and de-risk further development milestones. Following this, a proof-of-concept was implemented by designing and executing multiple experimental setups within AWS, simulating a production-ready RAG workflow. The outcomes of these tests served as a critical input for the client's broader feasibility assessment, confirming technical viability and identifying optimization potential.
Technologies & Frameworks
Applied Cloud & AI Stack for Document Intelligence
AWS S3 – for storing and accessing large volumes of technical documentation in a scalable way
AWS Lambda – to run lightweight, serverless data transformation and validation jobs
Python (pandas, NumPy) – for data exploration, cleaning, and insight extraction from unstructured content
LangChain – to orchestrate the RAG pipeline and manage interactions between query, retrieval, and generation
OpenAI API – used as the LLM backend to generate context-aware responses from retrieved documents
FAISS (optional) – considered as vector store backend for document embeddings
Jupyter Notebooks – for prototyping and running experiments interactively, enabling fast iteration
More Works
GenAI RAG Prototyping
Development and validation of a Retrieval-Augmented Generation (RAG) prototype using AWS to assess the feasibility of leveraging internal technical documentation for AI-powered question answering.
Semiconductor Client
2025
Client Description
The client is a global semiconductor leader renowned for delivering high-performance, energy-efficient technology solutions across automotive, industrial, and consumer markets.
Problem
Fragmented Documentation Hindering AI Application
The client aimed to enable internal engineers and technical staff to query large volumes of technical documentation using natural language – for example, to understand design decisions, component specifications, or system limitations. However, there was no existing infrastructure or tooling in place to support such a solution. The available documents had not been validated for their suitability in a generative AI context, and it remained unclear whether a Retrieval-Augmented Generation (RAG) approach would yield accurate and reliable results. Additionally, the client lacked a method for prototyping or experimenting with such technologies in a controlled, secure environment.
Solution
Data Assessment and Prototyping of a RAG-Based Architecture on AWS
A structured data exploration phase was conducted to systematically validate the technical documentation's relevance, structure, and quality with respect to RAG requirements. Key insights were derived to define architectural boundaries and de-risk further development milestones. Following this, a proof-of-concept was implemented by designing and executing multiple experimental setups within AWS, simulating a production-ready RAG workflow. The outcomes of these tests served as a critical input for the client's broader feasibility assessment, confirming technical viability and identifying optimization potential.
Technologies & Frameworks
Applied Cloud & AI Stack for Document Intelligence
AWS S3 – for storing and accessing large volumes of technical documentation in a scalable way
AWS Lambda – to run lightweight, serverless data transformation and validation jobs
Python (pandas, NumPy) – for data exploration, cleaning, and insight extraction from unstructured content
LangChain – to orchestrate the RAG pipeline and manage interactions between query, retrieval, and generation
OpenAI API – used as the LLM backend to generate context-aware responses from retrieved documents
FAISS (optional) – considered as vector store backend for document embeddings
Jupyter Notebooks – for prototyping and running experiments interactively, enabling fast iteration
More Works
GenAI RAG Prototyping
Development and validation of a Retrieval-Augmented Generation (RAG) prototype using AWS to assess the feasibility of leveraging internal technical documentation for AI-powered question answering.
Semiconductor Client
2025
Client Description
The client is a global semiconductor leader renowned for delivering high-performance, energy-efficient technology solutions across automotive, industrial, and consumer markets.
Problem
Fragmented Documentation Hindering AI Application
The client aimed to enable internal engineers and technical staff to query large volumes of technical documentation using natural language – for example, to understand design decisions, component specifications, or system limitations. However, there was no existing infrastructure or tooling in place to support such a solution. The available documents had not been validated for their suitability in a generative AI context, and it remained unclear whether a Retrieval-Augmented Generation (RAG) approach would yield accurate and reliable results. Additionally, the client lacked a method for prototyping or experimenting with such technologies in a controlled, secure environment.
Solution
Data Assessment and Prototyping of a RAG-Based Architecture on AWS
A structured data exploration phase was conducted to systematically validate the technical documentation's relevance, structure, and quality with respect to RAG requirements. Key insights were derived to define architectural boundaries and de-risk further development milestones. Following this, a proof-of-concept was implemented by designing and executing multiple experimental setups within AWS, simulating a production-ready RAG workflow. The outcomes of these tests served as a critical input for the client's broader feasibility assessment, confirming technical viability and identifying optimization potential.
Technologies & Frameworks
Applied Cloud & AI Stack for Document Intelligence
AWS S3 – for storing and accessing large volumes of technical documentation in a scalable way
AWS Lambda – to run lightweight, serverless data transformation and validation jobs
Python (pandas, NumPy) – for data exploration, cleaning, and insight extraction from unstructured content
LangChain – to orchestrate the RAG pipeline and manage interactions between query, retrieval, and generation
OpenAI API – used as the LLM backend to generate context-aware responses from retrieved documents
FAISS (optional) – considered as vector store backend for document embeddings
Jupyter Notebooks – for prototyping and running experiments interactively, enabling fast iteration
More Works