RAG Systems in Production: From 40% Accuracy to 95% with LangSmith

Title: "RAG Systems in Production: From 40% Accuracy to 95% with LangSmith"

Subtitle: "How to trace, evaluate, and optimize Retrieval-Augmented Generation pipelines for production deployment"

Target Audience: Backend engineers, AI developers building production systems

Estimated Read Time: 18-22 minutes

Style: Same as MCP blog - technical but engaging, diagrams-heavy, production-focused

Blog Structure

1. Opening Hook: "The RAG Performance Gap"

Problem Story:

You build a RAG chatbot for your company's documentation.

Demo day: "What's our refund policy?" → Perfect answer
Week 1 production: "How do I cancel my subscription?" → Hallucinated garbage
Week 2: Users stop using it

What went wrong?

The Issue:

RAG demos look great (cherry-picked examples)
Production reveals: 40-60% accuracy
No visibility into WHY failures happen
Can't systematically improve

The Solution Preview: LangSmith transforms RAG from "demo magic" to production systems through:

Trace-level observability
Systematic evaluation
Data-driven optimization

2. RAG 101: What You Actually Need to Know

The Basics (Quick Refresher):

Loading diagram...

The Three Failure Modes:

Failure	What Happens	Example
Retrieval Failure	Wrong docs retrieved	Query: "pricing" → Gets "privacy policy"
Context Failure	Right docs, bad ranking	Relevant info on page 5 of 10 docs
Generation Failure	LLM ignores context	Has answer in docs, hallucinates anyway

Why This Matters: You can't fix what you can't see. Traditional logging shows inputs/outputs. LangSmith shows the ENTIRE pipeline.

3. The Observability Problem

What You're Flying Blind On:

# Traditional approach - black box
def rag_query(question: str) -> str:
    docs = retriever.get_relevant_docs(question)
    answer = llm.generate(question, docs)
    return answer  # 🤷 Why did it fail?

What You Can't See:

Which embedding model was used
What similarity scores were returned
Which chunks were retrieved vs ignored
How the prompt was constructed
Token usage and latency per step

Production Horror Stories:

Changed embedding model → accuracy dropped 30% (took 2 weeks to find)
Chunk size optimization → broke on edge cases (discovered via user complaints)
Prompt engineering → improved 80% of cases, broke 20% (no systematic testing)

3.5. Why LangSmith? (And Not Just Logging)

You might be thinking: "Can't I just add logging and call it a day?"

Short answer: No. Here's why LangSmith exists.

What Basic Logging Gives You:

import logging

logging.info(f"Query: {question}")
logging.info(f"Retrieved docs: {len(docs)}")
logging.info(f"Answer: {answer}")
logging.info(f"Latency: {latency}ms")

What You Still Don't Have:

Nested execution traces (what happened inside each step?)
Automatic cost tracking (how much did this query cost?)
Evaluation datasets (is this getting better or worse?)
Comparison across experiments (which prompt performed better?)
Production monitoring dashboards (are we hitting SLAs?)

LangSmith vs Alternatives:

Feature	LangSmith	W&B	Arize Phoenix	Custom Logging
Nested Traces	✅ Full depth	⚠️ Limited	✅ Good	❌ Manual
Auto Token Tracking	✅ Built-in	❌ Manual	✅ Built-in	❌ Manual
Evaluation Datasets	✅ Native	⚠️ Manual	⚠️ Manual	❌ None
LLM-as-Judge Evals	✅ Pre-built	❌ Custom	⚠️ Limited	❌ None
Production Monitoring	✅ Real-time	✅ Excellent	✅ Excellent	⚠️ Custom
A/B Testing	✅ Built-in	✅ Good	⚠️ Limited	❌ Manual
LangChain Integration	✅ Native	⚠️ Adapter	⚠️ Adapter	❌ None
Setup Time	5 minutes	30 minutes	15 minutes	Hours/Days
Cost	$	$$	$ (OSS)	$ (infra)
Learning Curve	Easy	Medium	Medium	High

LangSmith's Unique Features:

1. Zero-Code Tracing

# That's it. One environment variable.
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"

# Every LangChain call is now traced automatically

2. Automatic Cost Tracking

Trace: rag_query
├─ OpenAI Embedding: $0.0002
├─ FAISS Search: $0 (local)
└─ GPT-4 Generation: $0.0232
Total: $0.0234

You don't calculate this. LangSmith does.

3. Prompt Playground

Edit prompts in UI
Test on real production traces
Compare outputs side-by-side
Deploy winning prompts instantly

4. Human Feedback Loop

from langsmith import get_current_trace_id

# In production
trace_id = get_current_trace_id()

# User clicks thumbs down
client.create_feedback(
    trace_id,
    key="user_rating",
    score=0,
    comment="Answer was wrong"
)

# Later: Create dataset from low-rated traces
low_rated = client.list_runs(
    filter="feedback.user_rating == 0"
)

5. Datasets from Production

# Export failed production traces as test cases
client.create_dataset_from_runs(
    dataset_name="production-failures-jan-2026",
    run_ids=failed_run_ids
)

This is impossible with basic logging.

6. Collaborative Debugging

Share trace URL with teammates
Comment on specific steps
Tag traces for review
Async debugging (no screen sharing needed)

When to Use Each Tool:

Use LangSmith when:

Building with LangChain/LangGraph (native integration)
Need quick setup (5-minute onboarding)
Want evaluation datasets built-in
Iterating fast on prompts
Small to medium team (<50 people)

Use Weights & Biases when:

Already using W&B for ML training
Need advanced experiment tracking
Multi-modal models (images, audio, video)
Large enterprise with W&B contract

Use Arize Phoenix when:

Open-source requirement (self-hosted)
Custom embedding models
Need full data ownership
Budget-conscious (free tier generous)

Use Custom Logging when:

Simple use case (single LLM call, no RAG)
Non-LangChain stack
Existing logging infrastructure
Compliance requires on-prem everything

LangSmith's Killer Combo:

Tracing (observe)
    ↓
Datasets (collect)
    ↓
Evaluation (measure)
    ↓
Comparison (decide)
    ↓
Deploy (ship)
    ↓
Monitor (watch)
    ↓
Feedback (learn)
    ↓
Repeat

All in one platform. This is why LangSmith exists.

The Bottom Line:

You can build this yourself with logging + Postgres + Grafana + custom eval scripts.

Time investment: 2-4 weeks Maintenance burden: Ongoing LangSmith alternative: 5 minutes setup, $49/month

For most teams, LangSmith is the obvious choice. You're building AI apps, not observability platforms.

4. LangSmith Architecture: How It Actually Works

The Three Components:

Loading diagram...

Key Insight: LangSmith runs async. Zero latency added to your production app.

5. Setting Up Tracing (The 5-Minute Integration)

Step 1: Install & Configure

pip install langsmith langchain-openai

import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "lsv2_pt_..."  # from langsmith.com
os.environ["LANGCHAIN_PROJECT"] = "rag-production"

Step 2: Add @traceable Decorator

from langsmith import traceable
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

@traceable  # ← This one line enables tracing
def rag_query(question: str) -> dict:
    # Embedding (auto-traced by LangChain)
    embeddings = OpenAIEmbeddings()

    # Retrieval (auto-traced)
    vectorstore = FAISS.load_local("./db", embeddings)
    docs = vectorstore.similarity_search(question, k=5)

    # LLM call (auto-traced)
    llm = ChatOpenAI(model="gpt-4", temperature=0)
    context = "\n".join([d.page_content for d in docs])

    prompt = f"""Answer based on context only.

Context: {context}

Question: {question}"""

    answer = llm.invoke(prompt)

    return {
        "answer": answer.content,
        "sources": [d.metadata for d in docs]
    }

Step 3: Run It

result = rag_query("What's the refund policy?")
print(result["answer"])

What You See in LangSmith:

Loading diagram...

6. Understanding Traces: The Anatomy

A Real Production Trace:

Trace: rag_query
├─ Duration: 2,347ms
├─ Cost: $0.0234
├─ Status: Success
│
├─ [Step 1] OpenAIEmbeddings.embed_query
│   ├─ Input: "What's the refund policy?"
│   ├─ Model: text-embedding-3-small
│   ├─ Tokens: 8
│   ├─ Latency: 45ms
│   └─ Output: [768-dim vector]
│
├─ [Step 2] FAISS.similarity_search
│   ├─ Query Vector: [0.123, -0.456, ...]
│   ├─ Top K: 5
│   ├─ Similarity Scores: [0.89, 0.87, 0.85, 0.82, 0.79]
│   ├─ Retrieved Docs:
│   │   ├─ Doc 1: "Refund Policy - Full refund within 30 days..."
│   │   ├─ Doc 2: "Terms of Service - Section 4.2 Refunds..."
│   │   ├─ Doc 3: "FAQ - How do I request a refund?..."
│   │   ├─ Doc 4: "Customer Support - Refund process..."
│   │   └─ Doc 5: "Payment Methods - Refund timeframes..."
│   └─ Latency: 12ms
│
└─ [Step 3] ChatOpenAI.invoke
    ├─ Input Prompt: "Answer based on context only..."
    ├─ Context Length: 1,234 tokens
    ├─ Model: gpt-4
    ├─ Temperature: 0
    ├─ Response: "You can request a refund within 30 days..."
    ├─ Input Tokens: 1,234
    ├─ Output Tokens: 89
    ├─ Cost: $0.0221
    └─ Latency: 2,290ms

What This Reveals:

✅ Retrieval working (0.89 top score is strong)
✅ LLM got the right context
⚠️ But 2.3s latency is slow (optimization target)
✅ Cost per query: $0.023 (acceptable)

7. Building Evaluation Datasets

The Problem: Manual testing doesn't scale. You need systematic evaluation.

Solution: Create Test Datasets

from langsmith import Client

client = Client()

# Create dataset
dataset = client.create_dataset(
    dataset_name="rag-refund-policy",
    description="Test cases for refund policy queries"
)

# Add examples
examples = [
    {
        "inputs": {"question": "What's your refund policy?"},
        "outputs": {"answer": "Full refund within 30 days of purchase with proof of purchase."}
    },
    {
        "inputs": {"question": "How long do I have to request a refund?"},
        "outputs": {"answer": "30 days from the date of purchase."}
    },
    {
        "inputs": {"question": "Do I need a receipt for a refund?"},
        "outputs": {"answer": "Yes, proof of purchase is required."}
    },
    {
        "inputs": {"question": "Can I get a refund after 30 days?"},
        "outputs": {"answer": "Refunds are only available within 30 days."}
    }
]

for example in examples:
    client.create_example(
        dataset_id=dataset.id,
        inputs=example["inputs"],
        outputs=example["outputs"]
    )

Dataset Best Practices:

Dataset Type	Size	Use Case
Golden Set	10-20	Core functionality, regression testing
Edge Cases	20-50	Ambiguous queries, rare scenarios
Production Sample	100-500	Representative real-world queries
Adversarial	20-50	Jailbreak attempts, hallucination triggers

8. The Four Critical Evaluators

1. Correctness (Answer vs Reference)

from langsmith.evaluation import LangChainStringEvaluator

correctness = LangChainStringEvaluator(
    "labeled_criteria",
    config={
        "criteria": {
            "correctness": "Is the answer factually correct compared to the reference?"
        }
    }
)

What it checks: Final answer matches expected output

2. Relevance (Answer vs Question)

relevance = LangChainStringEvaluator(
    "qa",
    config={
        "criteria": "Is the answer relevant and helpful for the question?"
    }
)

What it checks: Answer actually addresses the user's question

3. Groundedness (Answer vs Retrieved Docs)

from langsmith.evaluation import evaluate

def groundedness_evaluator(run, example):
    """Check if answer is grounded in retrieved documents"""
    answer = run.outputs["answer"]
    docs = run.outputs.get("sources", [])

    # LLM-as-judge
    prompt = f"""Does this answer only use information from the provided documents?

Answer: {answer}

Documents: {docs}

Respond with YES or NO."""

    result = llm.invoke(prompt)
    return {"score": 1 if "YES" in result.content else 0}

What it checks: No hallucinations, stays within retrieved context

4. Retrieval Quality (Retrieved Docs vs Question)

def retrieval_evaluator(run, example):
    """Check if the right documents were retrieved"""
    question = run.inputs["question"]
    docs = run.outputs.get("sources", [])

    # Check if expected keywords appear in retrieved docs
    expected_keywords = ["refund", "30 days", "purchase"]
    doc_text = " ".join([str(d) for d in docs]).lower()

    matches = sum(1 for kw in expected_keywords if kw in doc_text)
    score = matches / len(expected_keywords)

    return {"score": score}

What it checks: Retriever is pulling relevant chunks

9. Running Evaluations

Complete Evaluation Pipeline:

from langsmith import Client
from langsmith.evaluation import evaluate

client = Client()

# Define evaluators
evaluators = [
    correctness,
    relevance,
    groundedness_evaluator,
    retrieval_evaluator
]

# Run evaluation
results = evaluate(
    lambda inputs: rag_query(inputs["question"]),
    data="rag-refund-policy",  # dataset name
    evaluators=evaluators,
    experiment_prefix="rag-v1-baseline",
    metadata={
        "model": "gpt-4",
        "embedding": "text-embedding-3-small",
        "chunk_size": 512,
        "retrieval_k": 5
    }
)

# View results
print(results.to_pandas())

Output:

Experiment: rag-v1-baseline
Dataset: rag-refund-policy (20 examples)

Evaluator         | Mean Score | Median | Min  | Max  |
------------------|------------|--------|------|------|
Correctness       | 0.75       | 0.80   | 0.40 | 1.00 |
Relevance         | 0.85       | 0.90   | 0.60 | 1.00 |
Groundedness      | 0.90       | 1.00   | 0.60 | 1.00 |
Retrieval Quality | 0.65       | 0.70   | 0.33 | 1.00 |

Overall Accuracy: 75%

Interpretation:

✅ Groundedness is high (low hallucination)
⚠️ Retrieval quality is weak (wrong docs)
🎯 Focus optimization on retrieval layer

10. Optimization Cycle: Baseline → Production

The Systematic Approach:

Loading diagram...

Real Optimization Example:

Iteration 1: Baseline

# chunk_size=512, k=5, no reranking
Accuracy: 75%
Retrieval Quality: 0.65

Iteration 2: Increase Retrieval K

vectorstore.similarity_search(question, k=10)  # was k=5
# More docs = better coverage

Accuracy: 78% (+3%)
Retrieval Quality: 0.72 (+0.07)
Cost: +15% (more tokens)

Iteration 3: Add Reranking

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor

compressor = LLMChainExtractor.from_llm(llm)
retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=vectorstore.as_retriever(search_kwargs={"k": 10})
)

Accuracy: 85% (+7%)
Retrieval Quality: 0.81 (+0.09)
Cost: +8% (reranking overhead)

Iteration 4: Optimize Chunk Size

# chunk_size=256 (was 512)
# Smaller chunks = more precise matches

Accuracy: 92% (+7%)
Retrieval Quality: 0.88 (+0.07)
Latency: -200ms (smaller context)

Iteration 5: Hybrid Search

# Add BM25 for keyword matching
from langchain.retrievers import EnsembleRetriever
from langchain_community.retrievers import BM25Retriever

bm25 = BM25Retriever.from_documents(docs)
ensemble = EnsembleRetriever(
    retrievers=[vectorstore.as_retriever(), bm25],
    weights=[0.7, 0.3]
)

Accuracy: 95% (+3%)
Retrieval Quality: 0.93 (+0.05)
Final Cost: +12% vs baseline

Final Results:

Metric	Baseline	Final	Change
Accuracy	75%	95%	+20%
Retrieval Quality	0.65	0.93	+43%
Latency	2.3s	2.1s	-9%
Cost/Query	$0.023	$0.026	+13%

Production Decision: +13% cost for +20% accuracy = Worth it

10.5. Metadata Filtering: The Underrated Optimization

Most RAG tutorials skip metadata filtering. That's a mistake. It's the highest-ROI optimization you can make.

The Problem:

# Without filtering: Search everything
question = "What's our refund policy for 2024?"
docs = vectorstore.similarity_search(question, k=5)

# Searches 10,000 documents:
# - 2023 policies (outdated)
# - 2022 policies (outdated)
# - Blog posts about refunds (not official policy)
# - Customer complaints (not policy)
# - 2024 policy (what we want)

# Result: Maybe gets 2024 policy, maybe doesn't

The Solution:

# With filtering: Pre-filter, then search
docs = vectorstore.similarity_search(
    question,
    k=5,
    filter={
        "document_type": "policy",
        "year": 2024,
        "category": "refund"
    }
)

# Searches only 50 documents (2024 refund policies)
# Result: Always gets the right policy

Real Impact:

Metric	Without Filter	With Filter	Improvement
Search Space	10,000 docs	50 docs	99.5% reduction
Retrieval Latency	120ms	15ms	87.5% faster
Accuracy	78%	91%	+13%
Cost	$0.026	$0.024	8% cheaper

Why It Works:

Reduces noise: Fewer irrelevant documents to search
Improves precision: Semantic search works better on focused dataset
Faster retrieval: Smaller search space = faster queries
Lower cost: Fewer tokens sent to LLM

Common Metadata Strategies:

# Strategy 1: Time-based filtering
# Use case: "What's the latest pricing?"
filter = {
    "created_date": {"$gte": "2024-01-01"},
    "document_type": "pricing"
}

# Strategy 2: Source-based filtering
# Use case: "Check our official documentation"
filter = {
    "source": {"$in": ["docs", "official_blog"]},
    "status": "published"
}

# Strategy 3: Category-based filtering
# Use case: "How do I cancel my subscription?"
filter = {
    "category": {"$in": ["billing", "subscription", "cancellation"]},
    "language": "en"
}

# Strategy 4: User-context filtering
# Use case: "What features do I have access to?"
filter = {
    "access_level": user.subscription_tier,
    "region": user.region
}

# Strategy 5: Multi-dimensional filtering
# Use case: "Latest API docs for Python SDK"
filter = {
    "document_type": "api_docs",
    "sdk": "python",
    "version": {"$gte": "3.0"},
    "deprecated": False
}

Implementation Example:

from langchain_community.vectorstores import Pinecone
from langchain_openai import OpenAIEmbeddings

# Store documents with rich metadata
docs = [
    Document(
        page_content="Full refund within 30 days...",
        metadata={
            "document_type": "policy",
            "category": "refund",
            "year": 2024,
            "version": "2.1",
            "region": "US",
            "last_updated": "2024-01-15"
        }
    )
]

vectorstore = Pinecone.from_documents(docs, embeddings)

# Query with dynamic filters
def smart_retrieval(question: str, user_context: dict):
    # Extract intent from question
    if "latest" in question.lower() or "current" in question.lower():
        time_filter = {"year": 2024}
    else:
        time_filter = {}

    # Combine with user context
    filter_dict = {
        **time_filter,
        "region": user_context.get("region", "US"),
        "language": user_context.get("language", "en")
    }

    return vectorstore.similarity_search(
        question,
        k=5,
        filter=filter_dict
    )

Production Pattern: Filter First, Then Search

Loading diagram...

When NOT to Use Metadata Filtering:

Documents don't have structured metadata
Metadata is unreliable or inconsistent
Search space is already small (<100 docs)
Query intent is too broad to filter

Best Practices:

Design metadata schema upfront: Plan categories before ingestion
Validate metadata quality: Ensure consistency across documents
Index metadata fields: Performance depends on indexed filters
Test filter combinations: Some filters are more selective than others
Monitor filter effectiveness: Track how often filters improve results

Metadata Filtering Checklist:

Documents have at least 3-5 metadata fields
Metadata is validated during ingestion
Vector store supports metadata filtering (Pinecone, Weaviate, Qdrant)
Filters are indexed for performance
Filter logic is tested with evaluation dataset

Impact on Evaluation:

# Iteration 6: Add Metadata Filtering
vectorstore.similarity_search(
    question,
    k=5,
    filter=extract_filters(question, user_context)
)

Accuracy: 97% (+2%)
Retrieval Quality: 0.96 (+0.03)
Latency: 1.8s (-300ms)
Cost: $0.024 (-8%)

Metadata filtering is the rare optimization that improves accuracy, speed, AND cost simultaneously.

11. Production Monitoring

The Three Dashboards:

1. Performance Dashboard

Latency (P50, P95, P99)
Cost per query
Error rate
Throughput (queries/min)

2. Quality Dashboard

User feedback scores
Groundedness (anti-hallucination)
Retrieval quality
Answer relevance

3. Alerts & Anomalies

Latency spike (>3s)
Cost spike (>$0.05/query)
Quality drop (<80% accuracy)
Error rate (>5%)

Setting Up Alerts:

# In LangSmith UI: Monitoring → Alerts → New Rule

{
  "name": "High Latency Alert",
  "condition": "p95_latency > 3000ms",
  "window": "5 minutes",
  "webhook": "https://hooks.slack.com/..."
}

12. Advanced Patterns

Pattern 1: A/B Testing Prompts

# Test two prompt variations
results_a = evaluate(
    lambda inputs: rag_query_v1(inputs["question"]),
    data="test-dataset",
    experiment_prefix="prompt-A-formal"
)

results_b = evaluate(
    lambda inputs: rag_query_v2(inputs["question"]),
    data="test-dataset",
    experiment_prefix="prompt-B-casual"
)

# Compare in LangSmith UI
# Prompt A: 87% accuracy
# Prompt B: 92% accuracy
# Winner: Prompt B

Pattern 2: Production-to-Dataset

# Export failed production traces as test cases
client.create_dataset_from_runs(
    dataset_name="production-failures-jan-2026",
    run_ids=failed_run_ids
)

# Now you can regression test fixes

Pattern 3: Multi-Model Evaluation

models = ["gpt-4", "gpt-4-turbo", "claude-3-opus"]

for model in models:
    evaluate(
        lambda inputs: rag_query(inputs["question"], model=model),
        data="test-dataset",
        experiment_prefix=f"model-{model}"
    )

# Compare: gpt-4 (95%), gpt-4-turbo (93%), claude (91%)
# Decision: Keep gpt-4

13. Common Pitfalls & Solutions

Pitfall 1: Evaluation Dataset Drift

Problem: Test on old data, deploy to new queries
Solution: Monthly dataset refresh from production traces

Pitfall 2: Overfitting to Evaluators

Problem: Optimize for LLM-as-judge, ignore user satisfaction
Solution: Mix automated + human feedback

Pitfall 3: Ignoring Cost

Problem: 99% accuracy costs $1/query
Solution: Set cost budget, optimize within constraints

Pitfall 4: No Regression Testing

Problem: New optimization breaks old functionality
Solution: Golden dataset, always test before deploy

14. Production Deployment Checklist

Pre-Launch:

Baseline evaluation (>80% accuracy)
Cost analysis (<$0.10/query)
Latency check (<3s P95)
Hallucination testing (<5% groundedness failures)
Edge case coverage (50+ adversarial examples)

Launch:

LangSmith tracing enabled
Monitoring dashboards configured
Alerts set up (latency, cost, quality)
Gradual rollout (10% → 50% → 100%)

Post-Launch:

Daily quality checks
Weekly dataset updates
Monthly model evaluations
Quarterly architecture review

15. Key Takeaways

RAG without LangSmith:

Demo works, production fails
No visibility into failures
Can't systematically improve
Manual testing doesn't scale

RAG with LangSmith:

Trace every step (embedding → retrieval → generation)
Systematic evaluation (datasets + automated evaluators)
Data-driven optimization (A/B test, compare, deploy)
Production monitoring (catch regressions early)

The Numbers:

Baseline: 40-60% accuracy (typical RAG demo)
With evaluation: 80-85% accuracy
With optimization: 90-95% accuracy
Production-ready: 95%+ with monitoring

Time Investment:

Setup tracing: 5 minutes
Build dataset: 2-4 hours
First evaluation: 30 minutes
Optimization cycle: 1-2 days per iteration
Production monitoring: 15 min/day

ROI:

Faster debugging (hours → minutes)
Higher quality (75% → 95% accuracy)
Lower costs (eliminate wasteful iterations)
User trust (consistent, reliable answers)

16. Resources

Official:

Code Examples:

Complete RAG pipeline with tracing
Custom evaluators library
Production monitoring setup
A/B testing framework

Community:

Visual Assets Needed

RAG Pipeline Diagram (with failure modes highlighted)
LangSmith Architecture (trace flow)
Trace Anatomy (nested execution steps)
Optimization Cycle (flowchart)
Before/After Metrics (comparison table)
Production Dashboard (mockup screenshots)

Code Repository Structure

rag-langsmith-production/
├── baseline/
│   └── simple_rag.py
├── optimized/
│   ├── reranking_rag.py
│   ├── hybrid_search_rag.py
│   └── final_rag.py
├── evaluation/
│   ├── create_dataset.py
│   ├── evaluators.py
│   └── run_evaluation.py
├── monitoring/
│   └── production_alerts.py
└── README.md

Blog Length: ~8,000 words Diagrams: 5-7 Mermaid diagrams Code Examples: 15-20 snippets Tables: 5-7 comparison tables Read Time: 20 minutes

Target Outcome: Readers can build production-ready RAG systems with 90%+ accuracy using systematic LangSmith evaluation.