Nick Kljaic

Co-founder of @askhandle. Leveraging #AI to automate customer support.

[Nick Kljaic @nick_kljaic]

AI will likely replace human agents in many aspects of customer service. AI has advanced to the point where it can handle a vast range of customer inquiries, provide personalized responses, and resolve issues more quickly and efficiently than human agents

https://t.co/1itA72iBBy

[Nick Kljaic @nick_kljaic]

In RAG, a retriever encodes the query, performs vector similarity search (often followed by reranking), and injects selected passages into the model’s context window. The intelligence of the response comes from the quality of the retrieved context.

[AskHandle @askHandle]

Just dropped the official AskHandle Node.js SDK! 🚀

✅ AI-powered chats
✅ Chat room management
✅ Auto polling
✅ Zero dependencies
npm install askhandle-sdk
🔗 https://t.co/ROGeUPtTUa
#NodeJS #AI #DevTools #Chatbots

[Nick Kljaic @nick_kljaic]

Model distillation makes AI models smaller, faster, and cheaper by training a compact model to mimic a larger one. This enables efficient AI deployment on devices like smartphones without relying on powerful servers.

[Nick Kljaic @nick_kljaic]

Markdown stands out as a standard format for working with large language models because of its simplicity, compatibility, and ease of use. Its structured syntax improves clarity and helps ensure that data used to train or interact with LLMs is well-organized.

[Nick Kljaic @nick_kljaic]

The foundation of a good RAG system lies in the data it retrieves from. Using high-quality, relevant, and well-structured data sources is essential. Bad data means bad results.

[Nick Kljaic @nick_kljaic]

RAG-based AI doesn't just scan documents, it understands them. It turns text into smart embeddings, instantly finding the most relevant info for any query. The language model then uses that to craft clear, accurate answers.

[Nick Kljaic @nick_kljaic]

Running a large language model locally demands high upfront and ongoing costs for hardware, power, maintenance, and expert staff. Some may benefit at large scale, but for most, cloud services are more flexible, scalable, and cost-effective.

[Nick Kljaic @nick_kljaic]

To search your PDFs, AI first extracts the text. If it's a scan or image, it uses OCR to read it. Then it turns the text into vectors, numbers that represent meaning. This lets the AI search based on ideas, not just words.

[Nick Kljaic @nick_kljaic]

The way you ask questions influences AI performance. Clear prompts with precise language ensure the AI understands your request, reducing misunderstandings and saving time. Crafting thoughtful prompts improves response quality, making interactions easier and more productive.

[Nick Kljaic @nick_kljaic]

Teaching AI to refuse to answer when unsure reduces hallucinations. Fine-tuning trains models to recognize gaps in knowledge and respond with, "I'm not sure," instead of guessing, helping prevent made-up information.

[Nick Kljaic @nick_kljaic]

MoE architectures are a big deal because they offer a path forward for building more capable AI systems without needing exponential increases in compute power. They represent a shift from brute-force scaling to smart scaling, where efficiency and performance grow together.

[Nick Kljaic @nick_kljaic]

Multimodal AI represents a big leap forward, enabling computers to see, hear, and understand the world more like humans. It’s changing how we interact with machines and unlocking new possibilities in areas like self-driving vehicles and medical care.

[Nick Kljaic @nick_kljaic]

AI function calls enhance ux by translating natural input into real-time actions, such as data retrieval or service triggers. This links user intent to backend execution, making interactions faster and more dynamic through clear intent-function mapping and API integration.

[Nick Kljaic @nick_kljaic]

Fine-tuning begins with a pre-trained model, such as Llama for text or ResNet for images. A task-specific dataset is prepared to adjust the model’s parameters. Using backpropagation, the model’s weights update, refining performance with fewer iterations than training from scratch

[Nick Kljaic @nick_kljaic]

Writing effective prompts improves with practice. Start with clear goals, provide context, and iterate. The more intentional your prompts, the more value you’ll get. Keep experimenting!

[Nick Kljaic @nick_kljaic]

The pre-training phase is crucial for the base model as it enables proficiency in language at a general level. Without pre-training, a model would lack the foundational understanding of how language works.

[Nick Kljaic @nick_kljaic]

A parameter in an LLM refers to the weights and biases that shape how it processes and generates text. These parameters define the model’s behavior, mapping inputs to outputs, and are adjusted during training to enhance performance.

[Nick Kljaic @nick_kljaic]

For LLMs, reasoning involves analyzing information, identifying relationships between concepts, and producing coherent and relevant responses. This capacity distinguishes LLMs from simpler AI systems, enabling them to tackle complex tasks and provide nuanced insights.

[Nick Kljaic @nick_kljaic]

RL in LLMs provides a framework for refining the model's behavior through feedback. The reward model acts as a teacher, guiding the LLM to produce outputs that are more aligned with desired qualities, such as accuracy and coherence.

[Nick Kljaic @nick_kljaic]

Reinforcement learning is generally better for problems needing long-term decisions, real-time adjustments, or handling uncertainty. Supervised fine-tuning is often more efficient when you have a labeled dataset and a well-defined output.