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Combining BM25 & Vector Embeddings for Better Retrieval in GenAI Solutions

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Combining BM25 & Vector Embeddings for Better Retrieval in GenAI Solutions

Description

Hybrid retrieval blends the strengths of keyword-based BM25 search with semantic vector embeddings to deliver more accurate and contextually relevant results. By pulling from both exact term matches and conceptually similar content, teams can construct retrieval pipelines that are more robust across diverse query types.

Why it's Important

No single retrieval method performs optimally in every situation. BM25 provides high precision for explicit queries, while vector embeddings uncover meaning and nuance beyond keywords. When used together, they minimize missed matches and irrelevant results-improving GenAI output quality, reducing hallucinations, and supporting explainability. As organizations scale GenAI across functions and formats, hybrid retrieval becomes essential for maintaining consistency, accuracy, and trust in the system’s responses.

Why it's Challenging @ Scale

  • Fusion logic is non-trivial: Balancing the weight of keyword and semantic signals requires careful design and experimentation.
  • Tooling fragmentation: Many platforms treat sparse and dense retrieval as separate systems, complicating orchestration.
  • Ranking conflicts: Relevance scores from BM25 and vector embeddings operate on different scales-making unified ranking difficult.
  • Increased latency and cost: Combining retrieval methods may require querying multiple indices and performing post-processing.
  • Limited evaluation benchmarks: It can be hard to quantify hybrid performance improvements without custom metrics and test sets.

Complexity

High: Successfully implementing hybrid retrieval requires deep understanding of both search paradigms, thoughtful pipeline design, and tuning across use cases and data types.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Enterprise GenAI Retrieval workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Introducing Enterprise GenAI Retrieval Concepts
  • Linking Retrieval with Application Experience
  • Modeling Document Contexts and Sections
  • Embedding with Metadata for Precision
  • Defining KPIs for Retrieval Effectiveness
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Prototype a Hybrid Retrieval Stack: Combine BM25 and vector embedding models in a single pilot workflow (e.g., knowledge search or chatbot).
  • Compare Individual vs. Hybrid Retrieval Performance: Use test queries to evaluate precision and recall for BM25, vector, and combined outputs.
  • Implement Simple Fusion Logic: Start with score normalization or reciprocal rank fusion to rank blended results without overengineering.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • A Deep Dive into RAG Re-Ranking
  • A Deep Dive into Advanced RAG Re-Ranking Methods
  • A Deep Dive into Agent-Based Response Refinement for High-Quality GenAI Responses
  • A Deep Dive into Agent-based Report Generation for High-Quality GenAI Responses
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Review where hybrid retrieval is adding value and identify cases where one method dominates.
  • Define in-scope Processes and Guardrails: Establish when hybrid retrieval is appropriate, and where to default to sparse or dense search alone.
  • Close any Data or Measurement Gaps: Collect metrics on retrieval precision, latency, and downstream LLM output quality for hybrid setups.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Prioritize high-impact workflows where hybrid retrieval can significantly improve output quality.
  • Build Awareness and Finalize Enablers: Share configuration templates, performance benchmarks, and decision trees for hybrid usage.
  • Operationalize Your Comms Plan: Promote success stories and communicate evolving best practices across engineering and content teams.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Publish Hybrid Retrieval Design Patterns: Provide guidance on common fusion methods like weighted scoring, rank merging, or ensemble voting.
  • Standardize Evaluation Metrics: Define precision, recall, and qualitative scoring benchmarks for hybrid retrieval pipelines.
  • Develop a Decision Matrix for Retrieval Strategies: Help teams choose between BM25, vector, or hybrid methods based on query type or domain.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Integrate Hybrid Retrieval into Shared Platforms: Make hybrid configurations accessible via APIs or modular services for enterprise-wide reuse.
  • Equip Teams with Retrieval Experimentation Tooling: Provide sandboxes or dashboards that let teams test fusion strategies and compare outputs.
  • Promote Use Cases with Proven Impact: Highlight where hybrid retrieval has measurably improved response accuracy, user satisfaction, or system performance.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Showcase Before-and-After Retrieval Comparisons: Demonstrate how hybrid strategies reduced hallucinations or improved answer relevance.
  • Recognize Teams Championing Hybrid Innovation: Highlight contributions from engineering, data science, or content teams who advanced hybrid retrieval.
  • Share Fusion Strategies That Worked: Disseminate practical lessons on blending keyword and semantic results effectively.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Hybrid Retrieval Defaults into Platforms: Preconfigure fusion logic and retrieval modes in internal tools and orchestration layers.
  • Dynamically Adjust Based on Query Intent: Use classifiers or heuristics to automatically adjust retrieval weights or methods depending on input type.
  • Optimize Latency via Parallel Querying: Use efficient system design to reduce retrieval delays when querying both sparse and dense indices.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Auto-Tune Fusion Parameters: Apply A/B testing and relevance scoring to continuously refine the balance between BM25 and vector results.
  • Automate Failure Case Review: Identify when one retrieval method underperforms and recommend adjustments automatically.
  • Integrate Real-Time Re-Ranking: Apply LLMs or learned models to reorder hybrid results based on context, task, or feedback.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Benchmark Retrieval Performance Over Time: Track quality and usage of hybrid vs. standalone methods to guide long-term strategy.
  • Apply Hybrid Retrieval to New Modalities: Extend hybrid techniques to audio, image captions, tables, or structured knowledge bases.
  • Explore Retrieval-Generation Co-Optimization: Align retrieval strategy with LLM fine-tuning or prompting methods for best end-to-end performance.

Key "Watchouts"

  • Overcomplicating fusion strategies: Complex scoring formulas may sound elegant but can be brittle and hard to maintain.
  • Failing to normalize or align scores: BM25 and vector similarity values operate on different scales-ranking results naively can backfire.
  • Using hybrid retrieval everywhere by default: In some simple queries, sparse or dense alone may outperform a hybrid approach.
  • Neglecting observability and evaluation: Without clear metrics, it’s hard to tell when hybrid retrieval is helping or hurting.
  • Overlooking latency implications: Calling multiple retrieval pipelines increases response time if not carefully optimized.

Targeted Benefits

  • Improved retrieval robustness: Covers both exact match and semantic intent, minimizing blind spots.
  • Higher-quality GenAI outputs: Better context leads to clearer, more relevant, and accurate responses.
  • Reduced hallucinations and noise: Complementary methods improve content precision and cut irrelevance.
  • Greater flexibility across use cases: Adaptable to structured, semi-structured, and unstructured domains.
  • Stronger retrieval explainability: Easier to debug and justify retrieved content using dual evidence signals.

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Eddie
Accelerated Innovation

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