RAG Search: Retrieval-Augmented Generation for Accurate, AI-Powered Search

Traditional search—whether keyword-based or vector-based—retrieves relevant documents, but it cannot generate answers or synthesize information. LLMs can generate answers, but without grounding, they often produce hallucinations or confidently incorrect responses.

This gap between retrieval and generation has driven the rapid rise of RAG (Retrieval-Augmented Generation) as the new standard for intelligent search systems.

Industry data shows:

• Over 60% of new enterprise AI applications now incorporate RAG to improve accuracy, relevance, and trust.
• LLM hallucinations drop by 40–70% when responses are grounded in retrieved, context-rich data.

RAG search combines semantic retrieval and LLM reasoning in a single pipeline, allowing systems to deliver accurate, contextual answers—not just documents. It represents the next evolution of search: faster, smarter, more reliable, and far better aligned with how humans naturally ask questions.

What Is RAG Search?

RAG Search—short for Retrieval-Augmented Generation Search—is an AI-driven search architecture that combines semantic retrieval with LLM-based answer generation. Instead of returning a list of documents, RAG retrieves the most relevant information and uses a Large Language Model to synthesize a precise, grounded answer.

At its core, RAG Search operates on two layers:

1. Retrieval Layer (Vector Search):
   The system converts the query into embeddings, performs similarity search over a vector index, and fetches the most relevant text chunks, product attributes, or documents.
2. Generation Layer (LLM):
   The retrieved context is passed into an LLM, which then generates a coherent, contextual answer grounded in the retrieved data.
   
   

This architecture ensures that the LLM doesn’t “guess” answers—it reasons over real, retrieved context.
The result is a search experience that is:

• More accurate than traditional LLM outputs
• More intuitive than keyword or vector search alone
• Better suited for complex, conversational, or multi-step queries

In simple terms:
RAG Search lets AI answer questions based on your actual data, not its imagination.

The RAG Search Architecture (Deep Technical Breakdown)

RAG Search operates through a multi-stage pipeline designed to retrieve high-quality context and use it to generate grounded, low-hallucination responses. Each stage directly affects accuracy, relevance, latency, and overall user experience. Below is the architecture broken down into its core components.

1. Query Embedding & Vector Retrieval Layer

When a user submits a query, the system first transforms it into a numerical representation (an embedding). This enables semantic matching instead of relying on exact keywords.

Key Components:

• Embedding Models: Transformer-based models (OpenAI, Cohere, Instructor, MiniLM, etc.) generate dense vectors.
• Vector Index: Stores embeddings of all your documents, product data, FAQs, manuals, PDFs, or structured fields.
• ANN Search (Approximate Nearest Neighbor): Algorithms such as HNSW, IVF, or PQ perform fast similarity search across millions of vectors.

Why it matters:
High-quality retrieval is the foundation of strong RAG performance. If retrieval fails, generation will hallucinate—even if the LLM is state-of-the-art.

2. Chunk Retrieval & Context Selection

RAG never retrieves entire documents. Instead, it retrieves chunks—smaller, semantically meaningful segments.

Critical design factors:

• Chunk size: 200–500 tokens is common; too large = noise, too small = lost meaning.
• Overlap: Helps preserve continuity for multi-sentence concepts.
• Top-K retrieval: Determines how many chunks are pulled (typically K = 3–10).

Context Selection Logic:

• Relevance scoring
• Metadata filtering
• Hybrid retrieval (keyword + vector)
• Deduplication and conflict removal

The goal is to isolate the most relevant, least noisy context for the LLM.

3. Context Re-Ranking & Prioritization

Before handing content to the LLM, many RAG systems apply re-ranking to improve accuracy.

Re-ranking methods include:

• Cross-Encoders: Compare the query with each chunk more precisely than embeddings alone.
• Hybrid scoring: BM25 (keyword relevance) + vector relevance.
• Rule-based prioritization: Document type, freshness, category, product metadata, etc.

Why it matters:
This step dramatically improves retrieval precision, especially for long or ambiguous queries.

4. Prompt Construction & Context Assembly

The retrieved chunks are packaged into a structured prompt the LLM can understand.

Prompt engineering considerations:

• Clear separation of “context” vs “instructions.”
• Avoiding irrelevant content that may mislead the LLM.
• Compressing context when it exceeds token limits (summarization, prioritization).
• Formatting metadata or product attributes into structured blocks.

Goal:
Give the LLM the cleanest, most relevant context possible to minimize hallucinations.

5. LLM Reasoning & Grounded Answer Generation

The LLM reads the prompt, reasons over the retrieved context, and generates an answer.

Important mechanisms:

• Grounding: The LLM uses only retrieved context—not parametric memory—to answer.
• Controlled generation: Instructions force the model to cite, extract, or strictly base its answer on context.
• Answer types:
  • Direct answers
  • Summaries
  • Product recommendations
  • Step-by-step reasoning
  • Comparison tables

Why this step is powerful:
LLMs can synthesize, summarize, and analyze retrieved information in a way traditional search systems cannot.

6. Post-Processing, Verification & Guardrails

Advanced RAG architectures add a final layer to ensure reliability.

Examples of guardrails:

• Citation linking to retrieved chunks
• Hallucination checks (verifying answer against context)
• Structured output enforcement (JSON, attributes, bullet points)
• Policy constraints (content filtering, compliance rules)

For ecommerce specifically:

• Matching answers to real product inventory
• Ensuring compliance with product attribute boundaries
• Preventing speculative recommendations

Why This Architecture Matters

A RAG system is only as strong as its weakest component:

• Bad chunking → irrelevant context
• Weak retrieval → hallucinated answers
• Poor prompt design → noisy reasoning
• No guardrails → trust issues

A well-designed RAG pipeline becomes a reliable, scalable retrieval engine, capable of powering everything from ecommerce search to support assistants to enterprise knowledge retrieval.

RAG Search vs Traditional Search

Traditional search systems—keyword-based or even vector-based—are designed to retrieve information. RAG Search is designed to understand, retrieve, and generate answers grounded in real data. The difference is not incremental—it is architectural and functional.

Below is a focused comparison across the dimensions that matter most.

1. Retrieval Logic: Literal Matching vs Semantic Reasoning

Traditional Search

• Keyword search matches exact terms.
• Vector search matches semantic similarity.
• Both return lists of documents.
• The user must interpret the content manually.

RAG Search

• Retrieves semantically relevant chunks and synthesizes them into an answer.
• Understanding is not limited to keyword overlap or similarity scores.
• The LLM reasons over retrieved content to produce contextual explanations.

Implication:
Traditional systems stop at retrieval; RAG completes the reasoning loop.

2. Output Type: Documents vs Direct Answers

Traditional Search Output:

• Ranked list of pages, products, or documents.
• Helpful only if the user is willing to read and interpret.

RAG Search Output:

• A synthesized, grounded answer.
• Extracts the relevant part of a 50-page PDF and answers in 2 lines.
• Can format results into summaries, comparisons, tables, or step-by-step instructions.

Example:
Query: “How do I compare Model A and Model B jackets in insulation and breathability?”

• Traditional search: shows two product pages.
• RAG search: generates a direct comparison.

3. Handling Query Complexity

Traditional Search

• Struggles with multi-attribute, long-tail, or ambiguous queries.
• Requires exact keywords or clean metadata.

RAG Search

• Handles multi-constraint queries naturally.
• Understands intent, context, and attribute relationships.
• Performs reasoning across multiple documents.

Example:
“Waterproof hiking boots under $100 suitable for wet terrain.”
Traditional search breaks.
RAG search interprets all constraints.

4. Accuracy and Hallucination Behavior

Traditional Search:

• No hallucinations—it simply retrieves what exists.
• But retrieval errors = zero results or irrelevant results.

RAG Search:

• Can hallucinate if retrieval is noisy.
• However, hallucinations drop 40–70% when RAG grounding is applied correctly.
• More reliable for open-ended descriptive queries.

Trade-off:
RAG is more powerful but must be carefully designed to avoid ungrounded answers.

5. Data Types Supported

Traditional Search:

• Strong for structured or text-based data.
• Cannot summarize PDFs or blend content from multiple sources.

RAG Search:

• Works across structured + unstructured data:
  • PDFs
  • Manuals
  • Chat logs
  • Product attributes
  • Knowledge articles
• Can merge content across multiple sources into one answer.

6. Latency & Complexity

Traditional Search:

• Very fast, low compute.
• Simple infrastructure.

RAG Search:

• Higher latency due to retrieval + generation.
• Requires vector indexes, embedding pipelines, and LLM inference.
• Needs caching, re-ranking, and optimized chunking.

This is why RAG is a strategic choice, not a drop-in replacement.

7. Best Use Cases for Each

Traditional Search:

• Ecommerce catalog browsing
• Navigation queries
• Exact match search
• Quick product lookups
• Structured field-based searching

RAG Search:

• Product Q&A
• Deep knowledge retrieval
• Complex, multi-step queries
• Summaries across documents
• Support automation
• Comparing similar items

Summary Table: RAG Search vs Traditional Search

Dimension	Traditional Search	RAG Search
Output	List of documents	Generated, grounded answers
Understanding	Keywords or vectors	Full contextual reasoning
Query Complexity	Limited	Handles multi-constraint queries
Hallucination Risk	None	Reduced but depends on retrieval quality
Supported Data	Mostly text	Any structured or unstructured source
Best Fit	Navigation & discovery	Q&A, reasoning, comparisons

RAG Search Failure Modes

RAG Search is powerful, but it is far from foolproof. Most production failures are not caused by the LLM—they originate in the retrieval pipeline, data chunking strategy, embedding quality, or prompt construction. Understanding these failure modes is critical for building reliable, scalable systems.

Below are the most important, technically grounded failure modes teams encounter when deploying RAG in real-world environments.

1. Retrieval Noise: When Irrelevant Chunks Pollute the Context

RAG systems rely on retrieving text chunks before generation. If the retrieval layer pulls irrelevant or loosely related content, the LLM attempts to reconcile it—increasing hallucination risk.

Causes:

• Weak embeddings or outdated embedding models
• Poor chunking (too large, too small, or semantically broken)
• High K-value (top-K retrieval set too large)
• No semantic filtering or metadata constraints

Example:
Query: “Does this fabric shrink after washing?”
Retrieved chunk: “Machine wash recommended. Do not bleach.”
The LLM might incorrectly infer shrinkage behavior from unrelated washing instructions.

Impact:
The LLM generates plausible but incorrect claims because retrieval polluted the context window.

2. Over-Retrieval & Context Overload

Some teams try to “be safe” by retrieving large amounts of context.
This backfires.

Symptoms:

• LLM answers become vague or generic
• Key facts get buried
• Context window overflows → truncated chunks
• The LLM attends to wrong parts of the context

Technical Cause:
Attention dilution — LLM attention mechanisms degrade when fed excessively large or noisy context.

3. Under-Retrieval: Not Enough Context to Answer Precisely

Opposite of over-retrieval—RAG sometimes retrieves too few chunks or the wrong chunks entirely.

Causes:

• Embeddings missing critical nuances
• Poor chunk boundaries
• Faulty top-K ranking logic
• Keyword-heavy queries that semantic models misinterpret

Example:
Query: “Compare Model A and Model B based on insulation.”
Retrieved chunks talk about waterproofing, not insulation.
The LLM generates a comparison based on the only data it sees—leading to hallucination.

4. Embedding Drift After Model Updates

If embeddings are regenerated with a different model (or updated version), semantic space shifts, and retrieval becomes inconsistent.

Common in production systems:

• Initial index built with Model v1
• New embeddings with Model v3
• Vectors no longer align → retrieval breaks subtly

Result:
Precision collapses without obvious symptoms—making this a dangerous silent failure.

5. Chunking Errors: Bad Segmentation = Bad Retrieval

Chunking determines what gets indexed. Poor chunking strategies lead to irrelevant, fragmented, or contextless retrieval.

Chunking failures include:

• Splitting mid-paragraph → loss of meaning
• Chunks too small → insufficient semantic signal
• Chunks too large → excessive noise
• No overlap → missing transitional context

Example:
FAQ document:
If “return policy” and “refund process” are split incorrectly, retrieval misses critical context.

6. Missing Metadata Signals in Retrieval

Many RAG pipelines are “embeddings-only.”
This causes failures when metadata should influence retrieval—such as product category, version, or language.

Example:
User searches for “size guide for the blue variant”.
Embeddings retrieve a general size guide for all products, ignoring variant-specific notes.

Solution:
Hybrid ranking = vector similarity + metadata filters + keyword matching.

7. The “Answer-Around” Problem (LLM Bypassing Context)

Sometimes, even with correct retrieval, the LLM uses its parametric memory to answer the question instead of referencing context.

This happens when:

• Prompt doesn’t force strict grounding
• Retrieved context is vague
• LLM is extremely confident about the topic

Impact:
Hallucinations reappear—even though the retrieval was correct.

8. Conflicting Context Leading to Contradictory Answers

If retrieved chunks contain conflicting information, the LLM tries to reconcile them logically—often incorrectly.

Example:
Chunk 1: “Product is water-resistant.”
Chunk 2: “Product is waterproof.”
Chunk 3: “Not suitable for heavy rain.”

The LLLM may incorrectly assert:
“The product is waterproof and suitable for all weather conditions.”

Why?
Models tend to merge conflicting details into a smooth narrative.

9. Latency-Induced Timeouts in High-Traffic Systems

Retrieval + LLM generation = expensive.
At scale, systems hit latency failures:

• Vector search degradation
• Long queue times for the LLM
• Multi-hop retrieval slowing responses

When retrieval fails due to timeout, some systems fallback to:

• answering without context → hallucinations
• returning empty results → UX failures

10. Access Control & Permission Failures (Dangerous for Enterprise)

If RAG is not permission-aware, it may retrieve context from documents the user is not allowed to see.

Example:
Employee-facing RAG retrieves content from HR or legal documents when used by a customer-facing agent.

This is one of the biggest blockers to enterprise adoption.

Evaluating RAG Search Quality

Evaluating RAG systems is uniquely challenging because it requires measuring two intertwined components:

1. Retrieval quality (Did we fetch the right context?)
2. Generation quality (Did the LLM generate the correct answer based on that context?)

Unlike traditional search systems, where relevance is the primary metric, RAG introduces dimensions like faithfulness, context grounding, and hallucination resistance. A high-performing RAG pipeline must be precise, recoverable, and consistently aligned with the source data.

Below is a structured evaluation framework used by advanced AI teams.

1. Retrieval Evaluation (Does the system fetch the right information?)

Retrieval quality determines 80% of RAG accuracy. Even the strongest LLM cannot compensate for missing or irrelevant context.

Key Retrieval Metrics

• Recall@K (Critical Metric)

Measures whether the relevant chunk appears in the top K retrieved items.

Formula:
Recall@K = 1 if the relevant chunk is in top K results; else 0

For long-form text or large catalogs, teams evaluate:

• Recall@1
• Recall@3
• Recall@5
• Recall@10

Target: Recall@5 ≥ 80% for high-confidence pipelines.

• MRR (Mean Reciprocal Rank)

Evaluates how high the relevant chunk appears in the retrieval ranking.

If the correct answer appears in rank r:
Score = 1 / r

Higher MRR = better ranking.

• Precision@K

Measures how many of the retrieved chunks are actually relevant.

Useful when K is large and retrieval noise is a concern.

• Query Coverage

Percentage of queries that return any meaningful results.

Low coverage indicates:

• Weak embeddings
• Poor chunking
• Bad index construction

• Embedding Drift Checks

Ensures semantic space consistency when embeddings models change or indexes are rebuilt.
A drift detector compares:

• Cosine similarity distributions
• Cluster density
• Cross-model embedding variance

If drift spikes → retrieval instability → hallucinations.

2. Generation Evaluation (Does the LLM produce grounded, accurate answers?)

Even if retrieval succeeds, generation can fail due to:

• Over-summarization
• Misinterpretation
• Ignoring context
• Conflicting retrieved information

Key Generation Metrics

• Faithfulness (Most Important)

Measures whether the answer is strictly grounded in retrieved context.

Questions to evaluate:

• Did the LLM cite or reference the right chunks?
• Did it introduce facts not found in retrieved context?

Faithfulness is commonly evaluated with:

• Automated hallucination detectors
• Factual consistency scoring
• Manual evaluation on golden test sets

• Answer Accuracy

Does the generated answer actually solve the user’s query?

Different from faithfulness:

• An answer can be faithful but incomplete
• Or correct but not grounded

Accuracy = Completeness + Correctness + Relevance.

• Coherence & Structure

Is the answer:

• Easy to read?
• Structured logically?
• Free from contradictions?

Format consistency matters in production systems.

• Brevity & Relevance Penalty

LLMs tend to over-explain.
Production-grade RAG systems optimize for:

• Precision
• Directness
• Task alignment

Shorter, fact-based answers typically convert better in ecommerce and customer support.

3. Holistic Evaluation (End-to-End RAG Pipeline Performance)

Beyond retrieval and generation, we must evaluate the system as a whole.

• End-to-End Success Rate

A single metric combining:

• Retrieval success
• Grounding
• Answer accuracy
• Zero hallucination

For consumer-facing systems, E2E accuracy ≥ 85% is considered strong.

• Latency (Critical for Real-Time Search)

RAG introduces multiple compute layers:

• Embedding creation
• Vector search
• Re-ranking
• LLM generation

Latency is evaluated as:

• p50 (median)
• p90 (slower edge)
• p99 (worst-case)

Targets for ecommerce:

• p50 < 900 ms
• p90 < 1.4s
• p99 < 2s

Anything slower degrades user experience.

• Context Utilization Rate

Measures how often the LLM actually uses retrieved chunks.

If the LLM frequently ignores context, something is wrong in:

• Prompt format
• Chunk relevance
• Context ordering

• Token Efficiency

Lower token usage = lower cost.
Teams track:

• Tokens per answer
• Cost per 100 queries
• Overhead from retrieval noise

4. Human Evaluation (Irreplaceable in RAG Systems)

Automated metrics alone cannot capture:

• Nuance
• Reasoning quality
• Trustworthiness
• Domain-specific correctness

Human evaluation should include:

• Golden questions
• Adversarial queries
• Real customer queries
• Outlier analysis

Raters grade:

• Retrieval correctness
• Answer completeness
• Hallucination severity
• Tone and style

5. Continuous Monitoring & Drift Detection

RAG pipelines degrade over time due to:

• Changing catalogs
• Updated documents
• Shifts in query patterns
• LLM model updates
• Embedding drift

Monitoring systems must track:

• Retrieval failure spikes
• Hallucination increase
• Latency degradation
• Index corruptions
• Token cost inflation

Continuous evaluation is not optional—it is mandatory for production RAG.

RAG for Ecommerce & Product Discovery

Ecommerce search is fundamentally a decision-making problem, not a document retrieval task. Shoppers ask nuanced, context-rich questions that traditional search engines and static filters cannot interpret. RAG (Retrieval-Augmented Generation) brings a reasoning layer on top of vector search, enabling search systems to understand, retrieve, and explain information that helps shoppers make faster, more confident buying decisions.

Below are the specific ways RAG transforms ecommerce discovery.

1. Natural-Language Product Q&A (The Most Impactful RAG Use Case)

Shoppers ask questions that span product attributes, use cases, compatibility, fit, materials, and contextual scenarios.

Examples:

• “Is this jacket warm enough for sub-zero temperatures?”
• “Will these headphones work with an iPhone 14?”
• “Which moisturizer is best for oily skin in humid climates?”

Traditional search engines cannot interpret these intent-rich questions.
Vector search improves retrieval but still returns documents or PDPs, not answers.

RAG bridges this gap by:

• Retrieving relevant product attributes, reviews, FAQs, manuals
• Synthesizing them into a precise, trustable answer
• Eliminating guesswork for the customer

Impact:Higher PDP engagement, fewer customer support queries, reduced returns.

2. Attribute-Level Reasoning for Complex Products

Many ecommerce products have attributes buried in long descriptions, poorly structured specs, or inconsistent catalog data.

RAG allows the system to:

• Extract attributes on-the-fly
• Understand attribute relationships
• Summarize key differences
• Fill metadata gaps during retrieval

Example:
“How does the cushioning compare between Model A and Model B running shoes?”

RAG retrieves cushioning-related chunks from both PDPs and generates a comparison—something traditional search cannot do reliably.

3. Multi-Constraint & Conversational Query Handling

Shoppers increasingly search like they talk:

“Black waterproof trail-running shoes under ₹5000 for monsoon weather.”

This query contains:

• Color
• Function (waterproof)
• Category (trail-running shoes)
• Price
• Use-case context (monsoon weather)

Traditional search fails because:

• Filters are siloed
• Attributes are inconsistent
• Keyword search doesn’t understand context

RAG combines semantic retrieval + reasoning, enabling accurate results and explanations for such complex queries.

4. PDP-Integrated AI Assistants (Context-Aware Guidance)

Most product pages overwhelm customers with raw data.
RAG converts product data into interactive intelligence:

• “Explain this product in simple terms”
• “Compare this to the previous model”
• “Does this size run small?”
• “Will this fit a 6ft person?”

RAG assistants reduce friction by removing the need for manual scanning of specs and reviews.

5. RAG for Product Comparison & Buying Guidance

RAG can synthesize information across multiple products:

• Highlight differences
• Identify best option for the user’s need
• Explain recommendations with evidence

Examples:

• “Which is better for gaming: Model X or Model Y?”
• “What’s the best laptop under $1000 for designers?”

This transforms the PDP into a guided selling environment—similar to an expert salesperson.

6. Repairing Weak or Incomplete Product Data (A Hidden Superpower)

Most catalogs have missing attributes, unstructured descriptions, or inconsistent metadata.

RAG can:

• Infer missing details from related documents
• Extract attributes from long-form content
• Normalize ambiguous product descriptions
• Fill gaps that would normally break filters or search

This enables intelligent discovery even when data quality is imperfect—a massive unlock for ecommerce teams.

7. Smarter Merchandising Through Context-Aware Ranking

Merchandising traditionally relies on:

• Manual boosts
• Category rules
• Static sorting

RAG-powered pipelines can rank products based on:

• Product suitability for the query
• Contextual relevance
• Attribute match quality
• Buyer intent inferred from conversation

This enables intent-driven PLP ranking, not static logic.

8. Reducing Support Load Through AI-Driven Search

Many support tickets are essentially product questions:

• “Does this come with a warranty?”
• “Can I use this on sensitive skin?”
• “How long is the charging time?”

RAG surfaces these answers instantly from manuals, FAQs, and PDP content.

Brands see:

• 30–50% reduction in repetitive support queries
• Faster resolution time
• Improved customer trust

9. Strengthening Discovery When Catalogs Grow Large

The larger the catalog, the harder traditional search performs.
RAG thrives in high-SKU environments because it:

• Handles sparse metadata
• Understands context beyond keywords
• Normalizes inconsistent attribute naming
• Allows reasoning across many documents

This is why RAG is particularly powerful for:

• Electronics
• Fashion
• Beauty
• Home improvement
• Automotive
• D2C brands with UGC-heavy SKUs

Why RAG Is a Perfect Fit for Wizzy’s Ecosystem

RAG amplifies the value of Wizzy’s core components:

• Vector search provides the retrieval layer
• Smart filters become more accurate with stronger attribute extraction
• Semantic search becomes more conversational and contextual
• Autocomplete can surface intent-driven, reasoning-based suggestions
• Merchandising becomes smarter with query understanding
• Catalog enrichment becomes automated through attribute inference

By combining RAG with Wizzy’s existing search intelligence, you move from:
“Find products” → “Advise shoppers with facts and context.”

This elevates your position from search provider to product discovery intelligence platform.

Implementation Considerations 

This section assumes you already understand RAG theory. Below are pragmatic choices, trade-offs, and concrete defaults that reduce risk and accelerate production-readiness.

1. Architecture Patterns & Orchestration

Recommended pattern: Microservices + Orchestrator

• Ingress service (API gateway) receives queries → Embedding service → Vector DB (ANN) → Reranker service (optional) → Prompt assembler → LLM inference service → Post-processor → Response.
• Use an orchestrator (serverless workflow, Kubernetes + Argo Workflows, or a lightweight stateful orchestrator) to manage retries, timeouts, and fallbacks.

Key design choices

• Keep embedding generation and LLM inference separate (different scaling characteristics).
• Make retrieval idempotent and stateless to enable horizontal scaling.
• Decouple re-ranking and prompt assembly so you can A/B models and strategies without reindexing.

2. Data Pipeline & Indexing

Chunking

• Default: 200–400 tokens with 20–50 token overlap.
• Strategy: semantic chunking where possible (split at paragraph/heading boundaries), fallback to token-based chunking.

Indexing

• Store: chunk text, chunk_id, doc_id, metadata (source, timestamp, lang, category, product_id), vector.
• Use versioned indexes (index_v1, index_v2) so you can rollback after embedding/model updates.

Incremental updates

• Use append-only segments + background compaction to avoid full reindexes.
• For frequent updates (SKUs, specs), use streaming index writers (Kafka → index writer).

Re-embedding policy

• Re-embed on model upgrade or major data changes.
• For cost: incremental re-embed (new/changed docs first), schedule full re-embed during low-traffic windows.

3. Embeddings: Model Choice & Management

Model strategy

• Use a general-purpose embedding for broad retrieval (e.g., 768–1536 dims).
• Use domain-specific fine-tuned embeddings for high-value product domains if needed.

Pragmatic defaults

• Dimensionality: 512–768 balances quality & cost.
• Top-K retrieval: K = 5 (start), tune to Recall@5 ≥ 80%.
• Similarity metric: cosine for normalized vectors; dot if using un-normalized vectors.

Drift controls

• Maintain an embedding model registry with semantic compatibility checks before switching models.
• Run a drift test (sample queries) comparing old vs new embeddings’ retrieval overlap; set a minimum overlap threshold (e.g., 85%) before rollout.

4. Retrieval & Re-Ranking

ANN index

• HNSW for low-latency, high-recall. IVF+PQ for very large corpora with compression needs.
• Sharding: shard by product category or by doc_id hash for scale.

Hybrid retrieval

• Combine keyword (BM25) + dense retrieval: fetch top-N from both pipelines and union for re-ranking.

Re-ranking

• Use a cross-encoder or lightweight neural re-ranker for final ordering (trade latency for precision).
• Typical flow: dense top-50 → re-rank top-10 → pass to prompt.

Defaults

• DenseTopK = 50, BM25TopK = 50, Union → ReRankTopK = 10.

5. Prompting & Context Assembly

Prompt template (practical) — include system instruction, retrieved context, and explicit grounding constraint:

SYSTEM: You are an assistant that answers only using provided CONTEXT. If information isn’t in CONTEXT, reply “I don’t know; check sources.”

CONTEXT:

[DOC 1 metadata] : <chunk text>

[DOC 2 metadata] : <chunk text>

…

USER: <user query>

TASK: Provide a concise, evidence-based answer. Quote back source IDs for any factual claims.

OUTPUT_FORMAT: {“answer”: string, “sources”: [doc_ids], “confidence”: number}

Context packing

• Prioritize re-ranked chunks, then truncate oldest/lowest scoring to fit model token limit.
• When token limit hits, apply summarization of lower-priority chunks (one-shot summarizer model) to compress context.

6. Post-Processing & Guardrails

Hallucination checks

• Verify all factual assertions by string-matching claims against context slices (exact or fuzzy). If unverifiable, mark as “not found” or omit.

Structured outputs

• Enforce JSON schema for programmatic consumers (e.g., {answer, sources[], highlighted_snippet[]}).

Citation & traceability

• Include doc_id:char_range citations for every factual statement.

Policy / compliance

• Filter content via safety policy before returning. Enforce data access control using metadata filters during retrieval.

7. Performance, Caching & Cost Controls

Latency targets

• p50 < 900 ms, p90 < 1.4s for ecommerce UX. If LLM latency is primary cost, use cached answers for frequent queries.

Caching

• Two layers: retrieval cache (query → top-K IDs) and response cache (query+context hash → generated answer). TTL depends on source freshness.

Batching & concurrency

• Batch embeddings for multi-query workloads. For real-time single user queries, keep per-query latency low—do not batch at the expense of UX.

Cost control

• Use distilled LLMs for routine answers, larger LLMs for high-value or complex queries. Monitor token usage, and set budget alerts.

8. Scaling & Infra

Compute split

• Embedding generation: CPU or GPU depending on throughput.
• ANN search: CPU-bound; tune memory/CPU based on index size.
• LLM inference: GPU preferred for latency; use autoscaling based on queue length.

Scaling strategies

• Horizontal scale inference pods behind a queue (FIFO) with concurrency limits per GPU.
• Use priority queues for paying customers/SLAs.

High-availability

• Multi-AZ deployment for vector DB + replicas for index.
• Health checks and circuit breaker policies to fallback to graceful degradation (e.g., return top-K documents when LLM unavailable).

9. Security, Governance & Privacy

Access control

• Apply document-level permissions at retrieval time; never allow LLM to see documents the user isn’t authorized to access.

Auditability

• Log query → retrieved_doc_ids → prompt → LLM output → final response. Keep immutable traces for auditing.

PII handling

• Remove or redact PII prior to indexing; annotate sensitive fields and exclude from LLM input unless explicitly allowed.

10. Testing, Monitoring & CI/CD

Testing types

• Unit tests for chunking, embedding generation, and indexing.
• Integration tests that assert Recall@K on golden queries.
• Regression tests for prompt outputs on golden question set.

Monitoring

• Metrics: Recall@K, MRR, hallucination rate (from periodic human eval), E2E success rate, latency p50/p90/p99, token cost per query.
• Alerts: retrieval failure spike, drift detection, latency SLO breach.

CI/CD

• Canary rollout for new embedding models or LLM prompts with canary traffic (5–10%) and automatic rollback on metric regressions.

11. Observability & Continuous Improvement

Human-in-the-loop

• Provide “flag” button for users to mark bad answers. Route to a review queue to improve retrieval & prompt rules.

Feedback loop

• Use labeled failures to train re-ranker or tune embeddings. Maintain a dataset of “golden queries” for continuous evaluation.

A/B experimentation

• A/B test re-ranking strategies, K values, prompt templates, and model sizes; measure conversion metrics (PDP→cart, demo clicks).

12. Practical Defaults & Checklist (Quick Start)

• Chunk size: 200–400 tokens, overlap 20–50.
• Embedding dims: 512–768.
• DenseTopK: 50, ReRankTopK: 10.
• Recall@5 target: ≥ 80%.
• Latency targets: p50 < 900 ms, p90 < 1.4s.
• Prompt template: separate CONTEXT from TASK, enforce OUTPUT_FORMAT.
• Caching: retrieval results TTL = 5–15 min, response TTL = 1–60 min (depends on freshness).
• Drift check: run weekly (or on every model update).
• Canary rollout for model/embedding changes: 5–10% traffic.

13. Example: Minimal Pseudocode Orchestration

def handle_query(user_query, user_id):

    query_vec = embed(user_query)

    dense_ids = ann.search(query_vec, top_k=50)

    bm25_ids = bm25.search(user_query, top_k=50)

    candidate_ids = union(dense_ids, bm25_ids)

    reranked = reranker.rank(user_query, candidate_ids)[:10]

    context = assemble_context(reranked)   # apply metadata filters, dedupe, order

    prompt = build_prompt(user_query, context)

    answer = llm.generate(prompt)

    verified = verify_answer_against_context(answer, context)

    log_request(user_id, user_query, reranked, prompt, answer, verified)

    return format_response(answer, verified)

Final Notes

• Start small: deploy a RAG pilot on a subset of data (top categories) to prove impact.
• Prioritize observability and human feedback from day one.
• Treat embeddings and chunking as first-class citizens — they determine your system’s trustworthiness.

FAQs