Tokenizer, attention, pretraining and fine-tuning: build your own ChatGPT-style model step by step, no GPU needed
Master tokenization, embeddings, positional encoding, self-attention, QKV matrices, softmax and multi-head attention
Building LLM Applications with DSPy Replacing manual prompts with systematic optimization | 5.41 MB
Title: Building LLM Applications with DSPy Replacing manual prompts with systematic optimization
Author: Serj Smorodinsky, Brett Kennedy
Category: Natural Language Processing, Programming Languages, Artificial Intelligence
Language: English | 296 Pages | ISBN: 9781638358596
Building LLM Applications with DSPy Replacing manual prompts with systematic optimization | 5.41 MB
Title: Building LLM Applications with DSPy Replacing manual prompts with systematic optimization
Author: Serj Smorodinsky, Brett Kennedy
Category: Natural Language Processing, Programming Languages, Artificial Intelligence
Language: English | 296 Pages | ISBN: 9781638358596
Learn tokenization, attention, and transformers in Python and PyTorch, then train and chat with your own GPT model
AI Cost Playbook FinOps for LLM systems, token economics, caching, routing, and cost governance | 2.18 MB
Title: AI Cost Playbook FinOps for LLM systems, token economics, caching, routing, and cost governance
Author: Caio Incau
Category: Computer Technology, Nonfiction
Language: English | 206 Pages | ISBN: 9781808822155
LLM Evaluation and Alignment, The Foundational Ideas (MEAP V03) | 17.68 MB
Title: LLM Evaluation and Alignment, The Foundational Ideas (MEAP V03)
Author: Han Lee
Category: Nonfiction, Computers, Advanced Computing, Engineering, Computer Engineering, Artificial Intelligence, Programming
Language: English | 412 Pages | ISBN: 6610001304942
Prompt Injection & Jailbreaking: Offensive Llm Red-Teaming
Published 7/2026
Created by Dr. Amar Massoud
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 31m ) | Size: 2.6 GB
Hands-on offensive LLM security: jailbreaks, indirect injection, agent exploitation, and defenses in a sandbox.
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Llm Token Optimization
Published 7/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 42m | Size: 95.65 MB
Practical, hands-on techniques to cut token usage, cost, and latency in production LLM and RAG apps.
What you’ll learn
Measure token usage before and after every request, and attribute cost per feature and user
Write lean prompts and control model output to cut the most expensive tokens
Manage context and memory with budgets, sliding windows, and compression
Slash RAG token usage with smart chunking, low top-k, and re-ranking
Capture system-level wins: prompt and semantic caching, model routing, batching, and streaming
Monitor cost per query in production and stop regressions before the invoice Apply a repeatable token-optimization playbook to any LLM application
Requirements
Basic experience calling an LLM API from any provider
Comfort reading simple Python-style pseudocode
No machine-learning or research background required
Description
This course contains the use of artificial intelligence.
Every call to a large language model is billed by the token, and tokens quietly drive both your cloud bill and your app’s latency. This hands-on course shows engineers exactly how to measure, reduce, and control token usage across a real production stack — without sacrificing quality.
You’ll start with what tokens really are and why they map directly to cost and speed, then learn to count tokens before you ever send a request. From there you’ll optimize at every layer: writing lean prompts, controlling model output, managing conversation history and context budgets, and compressing what you send. You’ll dig into the place tokens explode most — retrieval-augmented generation — and learn smart chunking, retrieval limits, and re-ranking to keep context tight.
Then you’ll capture the big system-level wins: prompt and semantic caching, right-sizing and routing between models, and batching and streaming. Finally you’ll learn to monitor cost per query in production and finish with a capstone that optimizes a real chat-over-your-data app end to end, plus a reusable token-optimization checklist.
By the end you’ll be able to cut the token cost and latency of an LLM application by a wide margin, on purpose and measurably — a skill every team shipping AI features now needs.
Who this course is for
Software and ML engineers building features on large language models
Tech leads responsible for AI cost and latency
Anyone shipping RAG or chat-over-your-data applications who wants them cheaper and faster
https://rapidgator.net/file/0859a0b7bf52300ada9de5057b84e8c9/LLM_Token_Optimization.rar.html