@dmasin.bsky.social
🤖 So, how do we bridge the gap?
AI must move beyond "semantic similarity" and adopt reasoning approaches to handle:
1️⃣ Incomplete/misleading inputs.
2️⃣ Implicit, experiential knowledge.
3️⃣ Complex real-world scenarios.
We’re only scratching the surface. 🛠️
AI must move beyond "semantic similarity" and adopt reasoning approaches to handle:
1️⃣ Incomplete/misleading inputs.
2️⃣ Implicit, experiential knowledge.
3️⃣ Complex real-world scenarios.
We’re only scratching the surface. 🛠️
January 7, 2025 at 11:55 AM
🤖 So, how do we bridge the gap?
AI must move beyond "semantic similarity" and adopt reasoning approaches to handle:
1️⃣ Incomplete/misleading inputs.
2️⃣ Implicit, experiential knowledge.
3️⃣ Complex real-world scenarios.
We’re only scratching the surface. 🛠️
AI must move beyond "semantic similarity" and adopt reasoning approaches to handle:
1️⃣ Incomplete/misleading inputs.
2️⃣ Implicit, experiential knowledge.
3️⃣ Complex real-world scenarios.
We’re only scratching the surface. 🛠️
🔄 MULTI-TURN conversations = the real world
Support isn’t single Q&A. It’s multi-turn, flowing through phases:
1️⃣ Understand true intent.
2️⃣ Clarify missing details.
3️⃣ Provide answers & adapt.
RAG struggles here—it wasn’t designed for these dynamic, evolving exchanges.
Support isn’t single Q&A. It’s multi-turn, flowing through phases:
1️⃣ Understand true intent.
2️⃣ Clarify missing details.
3️⃣ Provide answers & adapt.
RAG struggles here—it wasn’t designed for these dynamic, evolving exchanges.
January 7, 2025 at 11:55 AM
🔄 MULTI-TURN conversations = the real world
Support isn’t single Q&A. It’s multi-turn, flowing through phases:
1️⃣ Understand true intent.
2️⃣ Clarify missing details.
3️⃣ Provide answers & adapt.
RAG struggles here—it wasn’t designed for these dynamic, evolving exchanges.
Support isn’t single Q&A. It’s multi-turn, flowing through phases:
1️⃣ Understand true intent.
2️⃣ Clarify missing details.
3️⃣ Provide answers & adapt.
RAG struggles here—it wasn’t designed for these dynamic, evolving exchanges.
🌀 Complex scenarios require reasoning
Imagine this: “I paid £1000 for a hotel, £300 was to be refunded. I added £100 for a restaurant bill but got only £150 back.”
A human sees the issue: the customer expected £200 but got £150. A RAG agent? Likely lost in irrelevant details.
Imagine this: “I paid £1000 for a hotel, £300 was to be refunded. I added £100 for a restaurant bill but got only £150 back.”
A human sees the issue: the customer expected £200 but got £150. A RAG agent? Likely lost in irrelevant details.
January 7, 2025 at 11:55 AM
🌀 Complex scenarios require reasoning
Imagine this: “I paid £1000 for a hotel, £300 was to be refunded. I added £100 for a restaurant bill but got only £150 back.”
A human sees the issue: the customer expected £200 but got £150. A RAG agent? Likely lost in irrelevant details.
Imagine this: “I paid £1000 for a hotel, £300 was to be refunded. I added £100 for a restaurant bill but got only £150 back.”
A human sees the issue: the customer expected £200 but got £150. A RAG agent? Likely lost in irrelevant details.
🚀 Building truly superhuman AI support agents is about enabling them to understand. While RAG (Retrieval Augmented Generation) helps avoid hallucinations, real-world customer support is far messier. Let’s dive into how AI must evolve to deliver magical support experiences. 🧵
January 7, 2025 at 11:55 AM
🚀 Building truly superhuman AI support agents is about enabling them to understand. While RAG (Retrieval Augmented Generation) helps avoid hallucinations, real-world customer support is far messier. Let’s dive into how AI must evolve to deliver magical support experiences. 🧵