In the ever-evolving landscape of artificial intelligence (AI), Retrieval-Augmented Generation (RAG), semantic search, and large language models (LLMs) have emerged as powerful tools for processing and retrieving information. While these technologies have proven their worth in a wide range of industries, they face unique challenges when applied to engineering work. This is largely due to the nuanced and highly technical nature of engineering language and data.
Here, we’ll explore why these tools often fall short in engineering contexts, why hybrid approaches are more effective, and how tools like Pinecone and vector databases play a critical role in optimizing search and retrieval in this domain.
The Promise and Pitfalls of RAG in Engineering
RAG combines the capabilities of LLMs with external knowledge bases to improve responses by augmenting generated content with relevant data. The process typically involves using a vector database to retrieve relevant documents and feeding them into an LLM for final output.
However, in engineering work, RAG faces a fundamental issue: semantic embeddings.
Semantic Limitations in Engineering Terms
Semantic search relies on understanding the “meaning” of a query or sentence, often using embeddings generated by LLMs like OpenAI’s models or custom-trained alternatives. These embeddings aim to capture the contextual meaning of text. While this works well for general-purpose queries, engineering terms and jargon often fail to fit neatly into these embeddings.
For example:
Phrases like “Shutdown Valve” or “Explosion Overpressure ” have specific meanings that depend heavily on domain expertise and the combination of those words
Embeddings trained on general data sources (like web content or Wikipedia) only partially capture these nuances— our estimates are around 80% accuracy at best.
This gap can lead to irrelevant or inaccurate results when using just semantic searches for retrieval, as engineering language often requires an understanding of how specific terms interrelate. Semantic Search is not complete
Engineering work often involves interconnected terminologies and relationships that semantic embeddings alone cannot capture. For example, a query about “hydrostatic pressure” might require knowledge of fluid dynamics, materials science, and structural engineering. General embeddings often miss these deeper connection
Given these challenges, a hybrid search approach is far more effective for engineering workflows. Hybrid search combines semantic search with traditional keyword-based search to overcome the shortcomings of purely embedding-based methods.
Hybrid systems use both vector-based retrieval for semantic relevance and traditional keyword-based search for precise matching of engineering-specific terms.
But…a big but
Key domain-specific terms are needs to be built manually and stored . and storing it and building it is the trick
Why Hybrid is Better:
Precision: Keywords ensure critical engineering terms are not misinterpreted. This really requires an engineering bag of words that go together
Context: Semantic search adds contextual understanding, even when technical language is used.
Why Use Vector Databases?
Fast Retrieval: Pinecone and similar vector databases are optimized for lightning-fast retrieval of top-k results based on embeddings. This is orders of magnitude faster than querying an RDBMS and running raw data through an LLM.
Handling Unstructured Data: Engineering data often exists in unstructured formats (e.g., PDFs, manuals, CAD files). Vector databases excel at indexing and retrieving this kind of data.
Relevance Scoring: Vector databases prioritize results based on semantic relevance, ensuring that only the most pertinent documents are retrieved.
Ok so hybrid search with a twist,we are a data company besides being a software company so get in touch with us and we will share with you a demo