Guide on Performance and Security for Advanced Production RAG: Part 6 – Pre-retrieval Optimization

The pre-retrieval phase is a critical stage where user input is processed and transformed into optimized vector search queries

RAG systems, which have shown great promise in development environments, can be particularly challenging to deploy in production environments. One major hurdle is ensuring access and security. In production, RAG systems must handle a large volume of user requests while maintaining the security and integrity of the data. This requires robust access controls, encryption, and monitoring, which can be difficult to implement and maintain. In contrast, development environments often have more relaxed security settings, making it easier to test and iterate on RAG systems without the added complexity of security protocols.

“The case for production-grade RAG systems in enterprises warrant much deeper scrutiny over system design, given performance, cost and security considerations.”

In this 9 part series, we discuss various system design considerations that directly impact RAG system performance, cost and security which serves as a guide for CTOs, CISOs and AI Engineers.

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Pre-retrieval filters and transformations for security and retrieval performance optimization

For RAG systems, the pre-retrieval phase is a critical stage where user input is processed and transformed into optimized search queries. This phase precedes the actual retrieval of information from vector databases and is often overlooked, yet it plays a vital role in ensuring the efficiency, security, and effectiveness of the entire search and retrieval process.

The pre-retrieval phase requires careful engineering and design considerations to filter out malicious queries, optimize search queries, and ultimately improve the overall user experience. By implementing effective filtering and transformation techniques, malicious queries can be detected and filtered out, significantly improving security. Moreover, optimized search queries lead to enhanced retrieval performance, reducing the time and resources required to retrieve relevant information. Ultimately, this results in a better user experience, as users receive more relevant search results that meet their needs. By recognizing the importance of the pre-retrieval phase and investing in its engineering and design, organizations can significantly enhance the overall effectiveness of their RAG systems.

Input Content Filtering

Input content filtering is a crucial step in the pre-retrieval phase of RAG systems, ensuring that user input is sanitized, relevant, and optimized for search queries. This process involves analyzing and processing user input to remove unnecessary or harmful content, extract valuable information, and transform it into a suitable format for querying vector databases.

Syntax and Semantic Analysis

Syntax and semantic analysis involve examining the structure and meaning of user input to identify potential errors, ambiguities, or malicious intent. This includes checking for proper syntax, identifying entities, and understanding the context and intent behind the query. By analyzing the syntax and semantics of user input, RAG systems can detect and filter out queries that are likely to return irrelevant or harmful results.

Keyword and Phrase Extraction

Keyword and phrase extraction involve identifying the most relevant terms and phrases in user input that accurately represent the search query. This process helps to eliminate noise and focus on the essential keywords that will yield the best search results. By extracting keywords and phrases, RAG systems can optimize search queries and improve retrieval performance.

Entity Recognition and Validation

Entity recognition and validation involve identifying and verifying specific entities mentioned in user input, such as names, locations, organizations, and dates. This process helps to ensure that search queries are accurate and relevant, and that retrieved results match the user’s intent. By recognizing and validating entities, RAG systems can improve the precision and relevance of search results.

Filtering out Special Characters and Stop Words

Filtering out special characters and stop words involves removing non-essential characters and common words that do not add value to the search query. Special characters, such as punctuation marks and symbols, can be removed or replaced to prevent errors or malicious queries. Stop words, such as “the”, “and”, and “a”, are common words that do not typically affect search results and can be safely removed to optimize query performance. By filtering out special characters and stop words, RAG systems can streamline search queries and improve retrieval efficiency.

Query transformation

Query transformation is a crucial step in the pre-retrieval phase of RAG systems, aimed at optimizing search queries to improve retrieval performance and relevance. This process involves applying various techniques to transform user input into optimized search queries that can effectively retrieve relevant information from vector databases.


Multi-querying involves generating multiple queries from a single user input, each tailored to capture different aspects of the search intent. This technique helps to cast a wider net and increase the chances of retrieving relevant results. By generating multiple queries, RAG systems can capture different nuances and contexts, leading to more comprehensive search results.

In practice, multi-querying can be achieved through various techniques, including:

  • Query templates: using pre-defined templates to generate multiple queries based on different combinations of tokens and contexts. For example, a template might replace a specific token with a synonym or add/remove a specific keyword.
  • Machine learning models: training machine learning models to generate multiple queries based on the input text. For example, a model might be trained to predict alternative queries based on the context and intent behind the input text. These models can be fine-tuned for specific domains or use cases to improve their effectiveness.

Query Rewriting

Query rewriting involves rephrasing user input into a more effective search query, leveraging knowledge of the vector database and search algorithms. To achieve this, RAG systems can employ various techniques, such as:

  • Syntactic analysis: parsing the user input to identify the underlying structure and relationships, and rephrasing it into a more optimal query.
  • Semantic role labeling: identifying the roles played by entities in the user input (e.g., agent, patient, theme), and rephrasing the query to better capture the intended meaning.
  • Entity disambiguation: resolving ambiguities in user input by identifying the specific entities referred to, and rephrasing the query to target the intended entities.
  • Search algorithm optimization: leveraging knowledge of the search algorithm’s strengths and weaknesses to rephrase the query in a way that maximizes its effectiveness.

By leveraging these techniques, query rewriting enables RAG systems to transform user input into a more effective search query, overcoming limitations and improving the precision and relevance of search results. This can be achieved through various methods, including:

  • Rule-based approaches: using predefined rules and patterns to rewrite queries.
  • Machine learning-based approaches: training models on labeled datasets to learn effective query rewriting strategies.
  • Hybrid approaches: combining rule-based and machine learning-based techniques to leverage their strengths.

Prior Conversation Stacking

Prior conversation stacking involves leveraging context from previous searches to inform and optimize the current search query. This technique helps to retain the context of a conversation involving a series of questions and responses. To achieve this, RAG systems can employ various methods, such as:

  • Contextual embedding: representing the conversation history as a vector embedding that captures the semantic relationships between previous searches and the current query.
  • Query chaining: linking successive queries together to form a chain of context, allowing the system to consider the entire conversation history when processing the current query.
  • Contextualized language models: using language models that incorporate context from previous searches to generate more informed and relevant search queries.
  • Session-based modeling: modeling the conversation as a session, where each query is considered in the context of the entire session, rather than in isolation.

By leveraging these methods, prior conversation stacking enables RAG systems to capture the nuances of a conversation and refine search results accordingly, leading to more accurate and relevant responses.

Query Expansion and Reduction

Query expansion involves adding relevant terms and phrases to the search query to capture more comprehensive results, while query reduction involves removing unnecessary terms to improve precision. These techniques help to strike a balance between recall and precision, ensuring that search results are both comprehensive and relevant.

Query expansion can be achieved through various techniques, including term extraction from relevant documents, synonym identification, and entity recognition. For example, a search query for “java programming” could be expanded to include terms like “java development”, “java coding”, and “java software engineering”. This expansion can be done using techniques like named entity recognition (NER) and part-of-speech (POS) tagging to identify relevant terms and phrases. Additionally, query expansion can also involve incorporating domain-specific knowledge and terminology to capture more specialized results.

Query reduction, on the other hand, involves removing unnecessary terms and phrases that may be diluting the search results. This can be done by identifying stop words, common phrases, and irrelevant terms that do not add value to the search query. For example, a search query like “what is the best java programming book” could be reduced to “java programming book” by removing the unnecessary terms “what is the best”. Query reduction can also involve using techniques like stemming and lemmatization to normalize terms and reduce them to their base form, further improving the precision of search results.

Query Classification and Categorization Query classification and categorization involve identifying the type and category of the search query, such as informational, navigational, or transactional. It’s important to note that the approach to classification and categorization should be tailored to the specific use case and system requirements, as different systems may require different categorization schemes. For example, an e-commerce search system may require categorization by product type, while a knowledge graph search system may require categorization by entity type. Once the category or classification is determined, it can be used for more deterministic filtering, such as in hybrid search approaches, enabling more precise and relevant search results.

Overall, production environment RAG systems presents the following key questions. Dive into each of the subtopics through the links below:

  1. API model access vs hosted model on self-managed instances
  2. Choice of model and precision as a trade-off between performance and running cost
  3. Choice of vector databases based on types of supported search algorithm and security options
  4. Data pipeline design for reliability, content safety and performance-focused data pre-processing
  5. Choice of chunking approach based on type of content: length, sentences or logical chunks
  6. Pre-retrieval filters and transformations for security and retrieval performance optimization
  7. Post-retrieval ranking and stacking approaches for performance and cost optimization
  8. Guardrail implementation with consideration for different modalities of inputs and outputs
  9. Logging mechanisms to facilitate performance, cost and security analyses

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