PRD.txt

Product Requirements Document (PRD)
Project Title

Selective Retrieval and Grounded Self-Refinement for Reliable Retrieval-Augmented Generation

1. Background & Context

Retrieval-Augmented Generation (RAG) is widely used to ground large language models with external knowledge. However, most RAG systems retrieve documents for every query, regardless of necessity. This static design results in:

unnecessary retrieval cost and latency,

noisy context injection,

hallucinations when retrieved evidence is weak or contradictory.

Recent work in agentic reasoning, tool usage, and self-refinement has shown promise, but these techniques are typically evaluated in isolation. There is a lack of systems-level research that integrates selective retrieval control, grounding validation, and iterative self-correction into a single, rigorously evaluated RAG architecture.

2. Problem Statement

Static RAG pipelines lack control and verification mechanisms. They cannot:

Decide whether retrieval is necessary.

Evaluate the adequacy of retrieved evidence.

Detect and correct hallucinations caused by weak grounding.

Quantify decision quality or failure modes.

As a result, existing systems trade efficiency for reliability without measurable justification.

3. Objectives
Primary Objective

Design and evaluate a model-agnostic agentic RAG system that improves factual reliability and efficiency through selective retrieval, grounding-aware validation, and self-refinement.

Secondary Objectives

Reduce unnecessary retrieval operations.

Explicitly detect and manage failure cases.

Provide transparent evaluation of decision quality.

Maintain reproducibility using standard benchmarks and pretrained components.

4. Success Criteria (Measurable)

The system is considered successful if it satisfies all of the following:

Matches or exceeds static RAG accuracy on benchmark tasks.

Demonstrates a statistically significant reduction in retrieval calls.

Reduces hallucination rate relative to static RAG.

Achieves higher decision accuracy than heuristic baselines.

Shows consistent gains in ablation studies.

No state-of-the-art claims are required.

5. Non-Goals (Explicit Exclusions)

Training or fine-tuning large language models

Reinforcement learning

Robotics or embodied environments

Novel datasets or benchmarks

Claims of autonomy or intelligence

This project is a systems and evaluation contribution, not a learning-theory contribution.

6. System Overview

The system is an agent-controlled RAG pipeline composed of six deterministic modules:

Agent Controller

Retrieval Decision Module

Retrieval Engine

Grounding & Feasibility Scoring

Answer Generation

Self-Refinement Loop

Each module corresponds to established prior work and is independently ablated.

7. Prior Work Integration (Explicit Mapping)
Retrieval Backbone

Motivated by REALM and implemented using Dense Passage Retrieval.

Dual-encoder dense retrieval

FAISS vector index

Top-k passage retrieval

No retriever training

Agent Controller

Based on ReAct.

Thought → Action → Observation loop

Deterministic step limits

Explicit action logging

Actions: retrieve, refine query, generate, abstain

Tool / Retrieval Decision

Inspired by Toolformer.

Binary decision: retrieve vs no retrieve

Decision inputs:

model confidence estimate

expected retrieval benefit

prior failure signals

Implemented via prompt-based control, not training

Grounding & Feasibility Scoring

Adapted from Do As I Can Not As I Say.

Relevance score (query–passage alignment)

Consistency score (inter-passage agreement)

Contradiction detection

Threshold-based acceptance or rejection

Self-Refinement Loop

Based on Self-Refine.

Triggered only when grounding improves

Bounded iteration budget

Refinement success tracked

Early termination on stagnation

Long-Horizon Design Principles

Structural inspiration from Voyager.

Stable agent loop design

Memory of past failures

No embodiment or skill learning

8. Detailed Workflow

User query received

Agent reasons about answerability

Retrieval decision made

If retrieval required:

Dense retrieval executed

Grounding and contradiction checks applied

Answer generated

Self-refinement triggered if grounding insufficient

Abstention allowed if grounding fails repeatedly

Final answer returned with evidence

All intermediate decisions are logged.

9. Functional Requirements
FR-1: Selective Retrieval

The system must decide whether retrieval is necessary for each query.

FR-2: Decision Transparency

The system must log retrieval decisions and confidence estimates.

FR-3: Grounding Validation

The system must reject answers lacking sufficient evidence.

FR-4: Self-Correction

The system must attempt refinement when grounding improves.

FR-5: Abstention

The system must abstain rather than hallucinate when evidence is insufficient.

10. Non-Functional Requirements

Deterministic execution

Reproducible results

Bounded latency

Modular design for ablation

Hardware-agnostic deployment

11. Evaluation Plan
Datasets

HotpotQA (multi-hop reasoning)

FEVER (fact verification)

Baselines

LLM without retrieval

Static RAG (always retrieve)

ReAct with always-retrieve

Metrics

Exact Match (EM)

F1 Score

Evidence precision

Hallucination rate

Retrieval calls per query

Token usage and latency

Decision-Quality Metrics

Unnecessary Retrieval Rate

Missed Retrieval Rate

Retrieval Decision Accuracy

Oracle policy comparison

12. Ablation Studies

Selective vs always retrieve

With vs without grounding

With vs without self-refinement

With vs without abstention

Single-pass vs agent loop

13. Failure Analysis (Required Section)

Failures are categorized into:

Retrieval failure

Decision failure

Grounding failure

Refinement failure

Each category is quantified and discussed.

14. Risks & Mitigation
Risk	Mitigation
Agent instability	Step limits, deterministic logic
Latency overhead	Selective retrieval
Overfitting heuristics	Oracle comparison
Reviewer skepticism	Strong ablations
15. Deliverables

Fully reproducible codebase

Experiment logs

System diagram

IEEE-formatted research paper

Ablation and failure analysis tables

16. Publication Positioning

Category: AI Systems / Applied NLP

Contribution type: Architecture + evaluation

Target venues: IEEE conferences and journals

Claims are conservative, evidence-driven, and reproducible.