What is LLM?
LLM(Large Language Model)
A Large Language Model (LLM) is a type of artificial intelligence model trained on vast amounts of text data to understand, generate, and manipulate natural language. Built on the Transformer architecture, LLMs use billions of parameters to recognize semantic patterns and reasoning relationships.
Why It Matters for AI Builders
Helps AI builders design and scale robust architectures; mastering the implementation of LLM improves latency, accuracy, and operational efficiency for conversational chatbots, text summarization, automated code generation, and semantic search translation.
Detailed Deep Dive
A Large Language Model (LLM) is a deep learning model trained on massive text corpora to understand, generate, translate, and reason over natural language. Typically built using Transformer decoder architectures with billions of parameters, LLMs utilize self-supervised pre-training to learn general language structures, which are subsequently adapted for conversational applications.
Frequently Asked Questions
Q:What does the "Large" in Large Language Model refer to?
It refers to both the massive size of the training datasets (often terabytes of text) and the high parameter count of the model (ranging from billions to trillions of weights).
Q:How do LLMs generate responses?
They generate text token-by-token. Given a prompt context, the model calculates the probability distribution for the next token and samples from it, recursively appending the output to generate sentences.
Quick Facts
- CategoryFoundational AI
- Key ApplicationConversational chatbots, text summarization, automated code generation, and semantic search translation.
Coverage Trend12 Weeks
Related AI Terms
Cite This Term
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LLM Media Coverage & Intelligence
Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving
Disaggregated LLM serving places compute heavy prefill and memory heavy decode on separate GPU pools. Systems such as DistServe, Splitwise, and Mooncake make...
Toward Governance-Aware Autonomous GIS: A Narrative Review of Ethical and Privacy Risks in LLM-Enabled GeoAI
Geospatial artificial intelligence (GeoAI) powered by large language models (LLM) is expanding the capacity to query, generate, and interpret spatial...
BLINDSPOT: A Benchmark for Safety and Refusal Calibration in Long-Horizon Tool-Using Agents
Large language model (LLM) agents increasingly operate over long-horizon interactions involving tool use, persistent state, evolving authorization, and...
Breaking the 1.58-bit Barrier for Ternary LLMs
Ternary Large Language Models (LLM) store every weight as one of three symbols $\{-1,0,+1\}$, so the cost of a ternary model is conventionally referenced to...
LLM-as-a-Judge Is Not an Oracle: Why Self-Improving Agents Need Deterministic Guardrails
Self-improving agent pipelines have a problem at their center. An optimizer rewrites prompt to score higher, and the score comes from a judge that is itself...
Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern
As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While statelessness...
Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems
Adapting the communication topology of an LLM multi-agent system to each query improves both accuracy and efficiency, yet current designers treat this as...
MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
LLM-based multi-agent system have shown strong performance on complex tasks, yet continual improvement from interaction experience remains challenging...
Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systems
LLM-based multi-agent system (MAS) are increasingly considered for high-stakes decision-making, yet outcome-based fairness audits can miss where risks arise...
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
The performance of an LLM agent depends on the scaffold around a frozen model. A common way to improve that scaffold is to use a coding agent as an...
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
Group-based reinforcement learning (RL) has become an effective paradigm for LLM post-training, but in multi-turn agentic tasks with sparse terminal rewards...
Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training
Large language models (LLM) often struggle when low-resource training data are ambiguous or incomplete. Task-level natural-language priors can provide...
Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics
Large Language Models (LLM) are increasingly deployed in high-stakes applications, yet their tendency to generate toxic, harmful, or policy-violating...
Rethinking the Evaluation and Optimization of LLM-Based Social Simulation
LLM-based social simulation is a promising complement to traditional methods such as surveys and behavioral experiments. A core question is how to evaluate...
Grok exfiltrates user data when malicious instructions are encrypted
Cryptographic Context Injection is only the latest way to break an LLM safety guardrail.
Position: Multi-Agent Systems Should Prioritize Concurrency Control
LLM-based multi-agent system (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues that many MAS...
Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories, use tools...
GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents
Clinical trial programming -- transforming study protocols into analysis-ready dataset under CDISC standards -- is a bottleneck in regulatory submissions...
Do LLMs Know a Good Hypothesis When They See One? Logit-Based Energy Scoring Outperforms Prompted LLM-as-Judge for Scientific Hypothesis Ranking
Large language models (LLM) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a challenge for...
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLM) must perform information calibration: matching content to a...
A decodability criterion predicts when hidden-state selection beats majority voting in large language models
Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by...
Don't Want Your LLM to Recommend Nuclear Strike? Try Asking It in Japanese
Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only. We test...
Z.ai debuts GLM-5.3 with long-horizon coding, cybersecurity upgrades
Chinese artificial intelligence developer Z.ai Co. today debuted GLM-5.3, an open-source large language model that set records across several popular benchmarks. The LLM is based on an algorithm called GLM-5.2 that the company released in mid-July. The latter model feature a mixture of experts...
LLM Optimization Integration for Amazon SageMaker Python SDK
The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook.
Alibaba debuts Qwen3.8-Max model with 2.4T parameters
Alibaba Group Holding Ltd. today debuted a new addition to its Qwen series of open-source large language models. Qwen3.8-Max is the Chinese e-commerce giant's most capable LLM to date. It feature 2.4 trillion parameters, about seven times more than the Qwen3.5 model that Alibaba released in...
Autoscaling endpoints for LLM inference
GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm. Here's how to pick autoscaling metrics, tune scale-up/down windows, and budget for cold starts on dedicated inference.
Keeping 100k battles of untrusted agent code in their lane
In March 2026, Lambda ran AgentBeats , an AI agent security competition in which teams submit two kinds of agents: an attacker that tries to manipulate a target LLM into doing something harmful, and a defender that tries to stay helpful while refusing the trap (check the final leaderboard here )...
Personalization, Personas, and Forecasting in Value Alignment
LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would...
Canadian legislator reads out apparent LLM response in floor speech
"Here's a more natural, flowing version of that section..."
Context assembly: building the prompt the model actually sees
The prompt a production LLM receives is almost never something a person wrote. By the time a request reaches the model, your app has stitched together system instructions, retrieved documents, conversation history, tool schemas, and stored memories in...
Why goodput matters more than throughput for LLM serving
When we benchmark an LLM serving setup, the number almost everyone reaches for first is throughput: how many requests per second the system can push through. It is easy to measure, easy to compare, and it...
Running a self-hosted LLM in Kubernetes with vLLM
Running large language model (LLM) workloads in-house is one of several patterns teams adopt alongside managed API services. Managed API services are convenient and well suited to many workloads. Self-hosting is a complementary option that some...
Disaggregated prefill and decode for LLM inference on SageMaker HyperPod
In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator.
Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline...
Google Updates Android Bench with New LLMs, but Gemini Still Lags Behind
Android Bench is evolving, and developers can help guide that process.
HippoRAG: Neurobiologically inspired RAG using Amazon Bedrock, Amazon Neptune, and personalized PageRank
In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph...
New attack provides one more reason why AI browsers are a bad idea
Telling an LLM that 2 + 2 = 5 is enough to make it follow forbidden instructions.
Implementing resilience patterns with Amazon Bedrock and LLM gateway
In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock feature...
DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%
Even as the geopolitical conversation around AI continues to grow more fraught following the U.S. government's actions to limit the new models from Anthropic...
Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS
In this post, we show you how PAR built a production-ready multi-tenant LLM analytics system that enforces row-level security through a three-layer...
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
Recent work shows that Large Language Models (LLM) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current...
OpenAI and Broadcom unveil LLM-optimized inference chip
OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.
Braintrust closes $30M Series A
AI evaluation and logging platform Braintrust has raised $30 million to expand automated testing workflows for LLM apps.
Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
Large language models (LLM) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel. We...
MLPerf Training v6.0: Lambda delivers fastest LLM training on NVIDIA GB300 NVL72 and fastest MoE training on NVIDIA HGX B200
Lambda's GB300 NVL72 Llama 3.1 8B MLPerf Training v6.0 submission improved performance by 18.7% over Lambda's previous result, achieving the fastest convergence on this round's workload on GB300 NVL72. In addition, Lambda achieved the fastest result among single-node HGX B200 submissions for...
Rewriting Your Pitch: SaaS Isn't Dead, But The Playbook For Founders Is Changing
Because AI and LLM are reshaping the traditional SaaS model, founders are forced to focus less on software alone and more on delivering measurable business...
Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit
Context window are becoming a computational bottleneck. The longer an agent runs, the more token accumulate from retrieved documents, reasoning traces and...
Making secret scanning more trustworthy: Reducing false positives at scale
Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The post Making...
Researchers say they trained a foundation model from scratch for about $1,500
Training a foundation LLM from scratch costs millions and requires internet-scale data - which is why most enterprises don't bother. Sapient thinks it has a...
Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution
When an LLM repeatedly mutates a program, does it explore new forms or circle back to the same ones? We study th
How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.
AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning
We present AgentJet, a distributed swarm training framework for large language model (LLM) agent reinforcement l
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning,
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
LLM agents accumulate histories that outgrow their context window, motivating a growing literature on memory sy
The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents
As autonomous AI agent move from conversational systems to long-horizon software execution, runtime safety laye
Airbnb's Brian Chesky plans to launch a new AI lab
The Airbnb CEO said last year it hasn't struck an LLM partnership because existing products weren't quite ready.
Alibaba's Qwen3.7-Plus supports text, video and imagery inputs at low cost of $0.4/$1.6 per 1M token - but it's proprietary
Alibaba this week released Qwen3.7-Plus , the latest AI large language model (LLM) in its globally beloved and increasingly expansive Qwen family, boasting...