1. Introduction & Objective
Decision Lens is an advisory system designed to ground AI-generated recommendations in peer-reviewed behavioral science and cognitive psychology literature. It assists professionals facing high-stakes decisions—such as executive strategy, governance, and investment—where the quality and consistency of judgment directly impact outcomes.
The system moves beyond generic AI responses by performing a dual-inference comparison: it generates a baseline answer (using the model's internal knowledge) alongside an evidence-grounded answer (retrieved from a curated corpus). This creates a measurable "bias lift," ensuring every recommendation is backed by traceable, verbatim source attribution.
2. The Challenge: Managing High-Stakes Judgment
High-stakes decision-making is often plagued by cognitive biases, such as confirmation bias or overconfidence. Traditional AI models may provide confident-sounding but unverified advice. The core challenges addressed include:
- Lack of Provenance: AI models typically struggle to explain *why* they suggest a specific intervention, leaving the human decision-maker without a defensible audit trail.
- Ingestion Noise: Broad knowledge bases often contain repetitive boilerplate, making it difficult to retrieve specific frameworks or study results efficiently.
- Context Blindness: Generic advice often fails to account for the specific organizational risk posture or practice profiles of the user.
3. Technical Architecture
The system architecture integrates three distinct layers to ensure output reliability and domain relevance:
A. Evidence-Grounded RAG Pipeline
The inference engine employs a hybrid retrieval strategy, combining dense vector search (Amazon Titan v2) with sparse keyword retrieval (BM25). These are merged using Reciprocal Rank Fusion (RRF) and MMR diversification to ensure that retrieved evidence is both highly relevant and structurally diverse.
B. Proxy-Pointer RAG & Knowledge Curation
To optimize performance, the system employs Proxy-Pointer RAG. Instead of blindly indexing raw text, a structure-guided pipeline parses and classifies passages (e.g., classifying text as "Studies," "Frameworks," or "Prescriptions"). This allows the retriever to prioritize high-yield content and prevents ingestion noise from diluting the retrieval precision.
C. Governance & Plugin Integration
Packaging the engine as a first-party plugin for platforms like Claude for Legal enables the use of strict practice-profile onboarding and reviewer-note discipline. This creates a governed workflow where output guardrails and taxonomic tagging make every advisory note auditable and defensible.
Curation Strategy: By creating a human-in-the-loop Corpus Curator interface, the system ensures that only vetted knowledge enters the index. This shifts knowledge base maintenance from an automated (and error-prone) task to a deliberate engineering workflow that focuses on high-fidelity, trusted content.
4. Engineering Insights
Development cycles focused heavily on making the "value of evidence" transparent and measurable. Key learnings included:
- Bias Lift Measurement: By comparing baseline inference against grounded retrieval, the system provides a clear metric of how much external evidence improved the decision-making context.
- Structured Chunking: The most significant retrieval gains came not from tuning embeddings, but from implementing structure-aware chunking that respects chapters and section logic rather than arbitrary word counts.
- Traceability Requirements: Implementing strict JSON schemas for run cards ensured that all findings are linked to specific provenance tags, preventing the system from producing unsupported "hallucinated" advice.
5. Broader Applications
Decision Lens establishes a blueprint for governed decision intelligence that can be adapted for any domain requiring measurable, audit-trail-backed judgment:
- Clinical and Medical Diagnostics: Adapting the curation engine to index clinical guidelines and peer-reviewed journals, allowing doctors to surface evidence-based treatment protocols alongside diagnostic suggestions.
- HR and Talent Acquisition: Leveraging the intervention playbook to help hiring managers mitigate unconscious bias during interview assessments by surfacing objective evaluation rubrics.
- Supply Chain and Risk Management: Using the RAG pipeline to ingest operational policy documents and risk frameworks, enabling supply chain leaders to generate structured, evidence-backed impact assessments when faced with logistics disruptions.
Let's Connect
I am keen to discuss the intersections of behavioral science, RAG architecture, and governed AI advisory systems. If you have inquiries regarding these technical design patterns or are exploring collaborative opportunities, feel free to reach out.
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