AI precision

Cracking AI Accuracy: AI Agents Empower Data Retrieval

Honghao Wang

— 3 min read 
# Precision Challenges in AI — From RAG to Agentic Workflows

## Spotting the Problem

**Adi Polak**:  
Look closely at the slide — you’ll notice odd mistakes like "AAI," or "precision" spelled with a double **I** or **S**. These subtle errors illustrate a core challenge in **generative AI**: achieving **true precision**.

Precision matters when:
- Moving from MVP prototypes to production systems
- Ensuring reliability and repeatability
- Avoiding errors that could have **legal or financial consequences**

---

## Real-World Risks of Imprecision

**Example: Air Canada chatbot**
- Tasks: Flight status, delays, seat changes, luggage queries
- Incident: Misled a customer on a business trip
- Consequence: Lawsuit due to incorrect information
- Impact: Financial loss + reputational damage

**Lesson:**  
Production AI demands **accuracy**. Fun experimental projects can tolerate minor errors — production deployments cannot.

---

## Role of Reliable Content Pipelines

As AI shifts from experimentation to deployment, reliable **content generation and publishing pipelines** are critical.  
Platforms like [AiToEarn](https://aitoearn.ai/) (open source) help:
- Generate **AI-powered content**
- Publish across **multiple platforms** (Douyin, WeChat, YouTube, LinkedIn, etc.)
- Track **analytics** and **model performance** ([AI模型排名](https://rank.aitoearn.ai))
- Scale **proof-of-concept** projects safely into production

---

## Precision — The Differentiator

Precision is **non‑negotiable** for production AI.  
Key questions:
- How do we **measure** precision?
- How do we **start** improving it?
- What methods work over time?

---

## Precision in ML vs. Generative AI

### Traditional ML
Basic formula:

Precision = True Positives / (True Positives + False Positives)

- **True Positives**: Correctly classified positive instances
- Divided by all predicted positives (correct + incorrect)

### Generative AI
Challenges:
- How does a model *know* if a generated output is correct?
- How do we define “precision” for LLMs or diffusion models?
- What methodology evaluates generated images/text/music/videos systematically?

---

## Tooling for Precision in Generative AI

Platforms like [AiToEarn](https://github.com/yikart/AiToEarn) are designed for:
- AI content generation
- Cross-platform publishing
- Analytics and model ranking

Docs: [AiToEarn官网](https://aitoearn.ai/) | [Documentation](https://docs.aitoearn.ai/)  

---

# Data-Centric Optimization

Two broad strategies for precision:
1. **Data-Centric Optimization**  
2. **Inference Optimization**

### RAG — Retrieval-Augmented Generation
- Grounds outputs with **real, current facts**
- Critical for dynamic contexts: finance, logistics, news

Integration with [AiToEarn](https://aitoearn.ai/) enables:
- AI content creation + retrieval grounding
- Publishing to **multiple channels**
- Tracking performance for continuous improvement

---

## Domain-Specific Fine-Tuning vs. RAG

**RAG**: Best for real-time data streaming  
**Fine-Tuning**: Best for batch/targeted domain inference  
Both improve precision given **quality data**

---

# How RAG Works

1. **User Query** — Input from user
2. **Retrieve** — Search external DB/knowledge base
3. **Augment** — Merge retrieved facts with the query
4. **Generate** — LLM produces final, context-rich output

> **Retrieve step quality** = output quality

---

## Retrieval Methods

**Term Search (TF-IDF)**  
- Requires exact term match  
- Best for traditional keyword queries

**Similarity Search (Vector Search)**  
- Uses embeddings + semantic similarity  
- Finds related meaning, not exact keywords

**Graph Search**  
- Uses relationships between entities

Modern systems combine **term**, **similarity**, and **graph** search for best results.

---

# Challenges in Retrieval

- Irrelevant or outdated data  
- Lack of real-time updates  
- Ambiguous queries  
- Retrieval latency

**Token limits** in augmentation phase can hinder response quality.  
In generation phase, hallucinations must be handled.

---

## Improving Early Retrieval Results

**Hybrid Search** — Choose best method per query  
**Re-ranking** — Adjust list of retrieved results for precision  
**Summarization** — Reduce token count before embedding  
**Prompt Refinement** — Avoid unnecessary RAG queries when possible

---

# Agentic RAG — Combining RAG with AI Agents

Evolution:
- Purpose-built AI → Generative AI → **Outcome-driven agents**

Agentic RAG features:
- Highly targeted
- Fine-tuned domains
- Specific tasks and tools

---

## Agent Architecture Example — BI Connector

Agents include:
- **Planner**
- **SQL Generator**
- **SQL Executor**
- **Judge** (validation/feedback)

Workflow:
1. Plan query  
2. Generate SQL  
3. Execute query  
4. Judge outcome

---

# Common Agent Patterns

**1. Orchestrator** — Break large tasks into subtasks  
**2. Hierarchical** — Multi-level delegation  
**3. Blackboard** — Shared memory for iterative improvement  
**4. Market-Based** — Auction-like competitive task assignment

---

## Continuous Improvement Techniques

- Feedback loops: thumbs up/down, surveys
- Human-in-the-loop review
- Memory storage & recall
- Reinforcement learning: reward/penalty system
- Benchmarking against SOTA metrics (Entity Precision, Exact Match)

---

# Scaling Challenges

Microservices for agent-based systems face:
- Tight coupling
- RFC/message loss
- Latency

**Solution:** Event brokers (Kafka) for:
- Exactly-once delivery
- Governance
- Real-time stream processing

---

## Governance in Streaming + AI

With RAG + DB access:
- Enforce access control
- Validate requests
- Ensure data quality
- Catalog + manage metadata

---

# Real-World Use Cases

**SDR Automation** — Accurate lead scoring  
**Marketing Automation** — Quality enforcement across content  
**Retail Digital Assistant** — Integrates cloud + store systems with Kafka  
**Cybersecurity** — Pushing accuracy from 95% to 100% using LLM-as-a-Judge

---

## Summary

We covered:
- Precision importance
- RAG fundamentals
- Retrieval optimization
- Agentic RAG
- Agent patterns
- Feedback loops
- Scaling architectures
- Governance
- Real-world implementations

---

**Integrated Tools:**  
[AiToEarn](https://aitoearn.ai/) — Open-source AI content monetization:
- Multi-platform publishing
- Analytics
- Model ranking ([AI模型排名](https://rank.aitoearn.ai))

---

## Q&A

**Q:** Risks of text-to-SQL?  
**A:** Use **templatization** to constrain queries. Limit table access + query structure to improve precision.

---

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