Companies gather vast amounts of internal knowledge — from technical reports and operational documents to communication archives. The key to intelligently leveraging these resources and creating AI that truly understands the organization is to effectively integrate access to this knowledge as the foundation. This enables the AI to rely on the unique organizational context, rather than just general public data.
Whether you’re planning to implement a modern recommendation system to support well-defined decisions or using retrieval-augmented generation (RAG) methods to answer various queries, search becomes the first step that determines the quality of results and impacts the efficiency of subsequent processes.
We draw from our experience in the Personal Advisor project—a solution that utilizes large sets of unstructured internal data—to demonstrate the considerable effort behind an effective implementation and how this approach brings real benefits to organizations.
Why Search is the First Step
RAG models, based on large language models (LLMs), enhance generative AI with real-world data. The success of this process depends on precise, context-aware data retrieval that supports response generation. A weak search layer leads to errors in data retrieval, resulting in irrelevant or nonsensical results.
Candidate Selection in Recommendation Systems:
Good search reduces the vast amount of data, which then feeds into ranking algorithms. Without this, even the best ranking algorithms won’t produce the expected results.
The Effort Behind Effective Search
Building an efficient search layer requires significant preparation, often underestimated. From data preparation to creating a scalable architecture—background work defines the quality of the results. Here are the key elements of this process:
- Data Preparation, which requires:
- Cleaning: Removing duplicates, irrelevant information, and inconsistent formats.
- Standardization: Converting data into structured formats to facilitate processing.
- Enrichment: Adding metadata such as tags, categories, or timestamps to enhance search capabilities.
- Preparing for Embedding Creation: Transforming raw, unstructured data (e.g., text documents, images) and customizing models for specific domains to capture nuances that general models miss.
Example from Our Project:
In our implementation for the energy sector, the data sets included technical reports, FAQs, and historical documentation. Extensive tagging (e.g., "emergency power shutdown," "backup systems") and formatting were necessary for compatibility with our vector search pipeline. - Creating Domain-Specific Knowledge
General search solutions often fail in specialized fields. Developing domain-specific knowledge bases and embeddings requires:- Understanding the Context: Adapting search mechanisms to interpret industry-specific vocabulary and jargon. For instance, in the energy sector, phrases like "peak load" or "integration of renewable energy sources" must be recognized as distinct concepts.
- Designing Taxonomies: Creating hierarchical relationships between tags, data ranges, and categories to improve filtering and ranking precision.
- Building Scalable Search Pipelines
Effective search systems must operate efficiently on large data sets. Key aspects include:- Indexing: Using technologies like vector databases for approximate nearest neighbor search to achieve low latency.
- Hybrid Approaches: Combining traditional keyword-based search with semantic search to balance precision and recall.
- Query Optimization: Dynamically filtering and prioritizing results based on user intent and metadata.
- Maintaining the Data Layer
- Knowledge Base Updates: Regularly adding new data and refreshing embeddings.
- Tuning Models: Adapting embeddings or ranking algorithms based on user feedback and performance metrics.
- Monitoring Performance: Tracking latencies, accuracy, and relevance of results to identify areas for improvement.
Key Takeaways and Best Practices
- Start with search.
- Appreciate the background work.
- Build with scalability in mind.
- Evaluate iteratively.
- Domain knowledge is key.
Author: Mariusz Jażdżyk
The author is a lecturer at Kozminski University, specializing in building data-driven organizations in startups. He teaches courses based on his book Chief Data Officer, where he explores the practical aspects of implementing data strategies and AI solutions.
