Explore the limitations of LLMs, the latest technologies to overcome them, and projected future technologies.
8 learners are taking this course
Level Beginner
Course period Unlimited
Current limitations of LLMs
The potential for development of LLMs
The Future of LLMs
Hallucinations, context windows, reasoning depth, knowledge cutoff, and consistency — we first isolate these five dimensions where LLMs face limitations, and then examine how technologies like RAG, Agents, MoE, Multimodal, Reasoning Models, Long Context, SLMs, and Alignment techniques are evolving to overcome them. This course is designed for intermediate-level developers who have used LLMs at least once, providing a coordinate axis of limitations, breakthroughs, and adoption through 13 sections and 93 lessons.
Those who have tried calling APIs or writing prompts but find it difficult to distinguish exactly which layer the limitation lies in when they say, "The LLM can't do this." Those who want to judge whether a specific issue, like a hallucination, should be solved through RAG or by switching to a reasoning model.
Those who need to bridge the gap where demos work well but production collapses due to cost, latency, and consistency issues. Those who want to accurately understand the three layers of inference-time control and the boundaries between fine-tuning and model replacement.
Those who need to be able to place the trends of multi-agents, MoE, multimodal, reasoning models, and small models on a single coordinate system and explain to their team which technology lineage offers the best cost-effectiveness for specific limitations.
Those who find themselves missing the big picture while reading papers and blog posts in fragments. Those who want to see limitations, technologies, and outlooks tied together in a single matrix within one lecture, creating a starting point to build subsequent in-depth studies upon.
Most LLM courses show you 'what works' first. This course starts the opposite way. Only by first breaking down the nature of the limitations can you finally see why subsequent technologies like RAG, Agents, and MoE were designed the way they are.
We have separated the path to bypassing a significant portion of limitations without changing a single line of model weights into three layers: Prompt, Context, and Harness Engineering, and gathered them into one section. This will prevent the mistake of resorting to expensive fine-tuning for problems that can be solved with prompts.
From Section 4 to 12, all breakthrough technique sections are structured with theory lessons and practical lessons paired within the same section. The conceptual understanding and hands-on coding are completed together in one place.
We categorize the limitations into three layers: model-specific limitations such as hallucinations, context windows, reasoning depth, knowledge cutoff, and consistency; application limitations such as cost, latency, and reproducibility; and social limitations such as bias, copyright, and accountability. This section serves to establish the coordinates for where all subsequent breakthrough technologies correspond.
Technological improvement directions, latest research trends, and everything from scaling, multi-modality, efficiency, and popularization to industrial expansion and the broadening of research topics. We first capture the big picture of development vectors that have emerged upon the coordinates of limitations.
Prompt Engineering (instruction design), Context Engineering (bypassing knowledge gaps with RAG), and Harness Engineering (loops, tools, and verification). We examine the interactions, investment priorities, and limitations of these three layers, then conclude with hands-on exercises on reducing hallucinations, RAG processing flows, and self-verification loops.
Agent patterns such as Orchestrator-Worker-Router, Plan-Execute-Verify, and Parallel Fan-out, along with a comparison of Claude Code, CrewAI, AutoGen, and LangGraph. Additionally, covering MoE's gate networks and expert activation visualization, spanning from Switch Transformer to Mixtral and DeepSeek.
Combining vision encoders with language models (GPT-4o·Gemini·Claude Vision), and multimodal hallucination detection. The transition from Chain-of-Thought to internal reasoning in o1·o3·DeepSeek-R1, visualization of reasoning steps, and performance comparison against general models.
Positional encoding extensions like RoPE, ALiBi, and YaRN, Ring and Flash Attention, and the "Lost in the Middle" phenomenon regarding nominal vs. effective length. In the opposite direction, small models like Phi and Gemma, knowledge distillation, quantization, pruning, and even on-device deployment.
Synthetic data generation and self-improvement learning, the risks of Model Collapse and quality control. RLHF·DPO·Constitutional AI alignment techniques, and the future of alignment such as Red Teaming·Scalable Oversight·Interpretability.
Combine the list of limitations from Section 1 and the breakthrough technologies from Sections 4–12 into a single matrix. This includes a technology maturity timeline, decision-making criteria by role (planner, engineer, executive), and a learning path guide leading to subsequent advanced courses.
"This is something LLM cannot do" is immediately classified into three layers—technical, application, and social—and five different dimensions.
Map RAG, Agents, MoE, Multimodal, Reasoning, Long Context, Small Models, and Alignment onto a single map.
The three layers of Prompt, Context, and Harness are evaluated alongside cost and reproducibility to be reflected in design decisions.
Determine the timing and priority of implementation using the Limitation × Breakthrough Matrix and the Maturity Timeline.
Experience using an LLM at least once (whether through API calls or a chat interface). Basic Python syntax (functions, exceptions, library imports). While the big picture of Transformers and pre-training will be briefly defined upon their first appearance, for deeper principles, we recommend the separate lecture 'Understanding the Basic Principles of Large Language Models (LLMs)'.
We have completely overhauled the entire structure, shifting from the original direction of a lecture for planners to one where both planners and developers can understand the current state of the latest LLM technology or apply it. We deleted the existing Section 4 (for planners) and added six new advanced sections. This course introduction page will be further updated as the course is finalized.
I have added the precautions and change history to the detailed introduction page.
Who is this course right for?
A planner who wants to understand the latest status of LLM technology
Developers who want to understand and apply the latest LLM technologies
709
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39
Reviews
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Answers
4.6
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18
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I am someone for whom IT is both a hobby and a profession.
I have a diverse background in writing, translation, consulting, development, and lecturing.
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105 lectures ∙ (5hr 49min)
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