LlamaIndex: Building RAG Applications with Multiple Data Sources

Increase efficiency and save money fine-tuning with Unsloth. #shorts #learnai #unsloth

Continued Pretraining and Fine-Tuning with Unsloth

DSPy forces you to think about your problem in very simple terms. #shorts #learnai #genai #training

Building agentic systems using DSPy AvaTar modules. #shorts #learnai #learning #genai

Production-Ready Optimization Strategies for RAG

How do we use DSPy with the AvaTar framework? #genai #learnai #learning #shorts

DSPy is a set of controls at the input level for better outputs #Shorts

DSPy: Advanced Agents?

Why Spectrum isn’t just another technique to optimize fine-tuning. #genai #finetuning #learngenai

When would you choose Spectrum over QLoRA, or when would you combine them? #genai #learnai #slm

Better Agents with LlamaIndex Workflows

How is the Spectrum system useful when training SLM’s? #shorts #genai #slm #training

What is an Agent IDE?

Q&A for Fine-Tuning Embeddings for Better Retrieval #shorts

What is Context Precision and how does it improve our fine-tuning actions? #finetuning #rag #genai

ERROR infinite loop detected! #llm #rag #genai #shorts

Fine-Tuning Embeddings for Better Retrieval

Using synthetic data is about helping us incrementally raise the bar on fine-tuning. #shorts #genai

We can use synthetic data at every layer of the stack! #shorts #sdg #genai #llm

Spectrum: Training Domain-Adapted SLMs

Drug Alert AI

Synthetic Data for Enterprise

Determining which models to use and learning from the history of model performance. #short

The AI Engineering Bootcamp Cohort 4