Tag: llm
28 articles filed under this tag. Newest first below ; start with the highlighted pick if you are new here.
Featured
Data Pipelines for LLM Training and Fine-TuningCleaning, deduplication, instruction formatting, tokenization choices, and dataset hygiene for supervised fine-tuning and preference tuning—emphasizing data quality as the dominant lever.
· 6 min read
- Streaming LLM Systems and Token-Level Response Design
How partial decoding and streaming protocols shape UX, back-end buffering, and client rendering—without coupling to any single provider’s wire format.
· 6 min read
- Building Real-Time Conversational AI Systems
WebSocket and HTTP streaming architectures, session memory, cancellation, and interruption handling for low-latency chat—where network, orchestration, and model tiers all shape the experience.
· 6 min read
- API Safety Design for AI Agents
Rate limits, permissioning, tool sandboxing, and execution boundaries for agent-facing APIs—where the agent runtime is a new class of client that amplifies abuse patterns.
· 6 min read
- Secure RAG Systems and Prompt Injection Prevention
How untrusted documents and web pages become indirect injection channels into retrieval pipelines—and how engineers harden ingest, retrieval, and tool boundaries without pretending RAG eliminates adversarial text.
· 6 min read
- Safety Layers in Production LLM Systems
Prompt injection defenses, output filters, policy enforcement, and sandboxing patterns that stack like defense in depth—because no single layer catches every abuse case.
· 6 min read
- Evaluation Frameworks for LLM Applications at Scale
Golden datasets, regression suites, LLM-as-judge patterns, and offline versus online evaluation loops—emphasizing measurement discipline over benchmark theater.
· 6 min read
- LLM Observability in Production Systems
Prompt and completion logging, distributed tracing, token accounting, feedback capture, and debugging pipelines for LLMOps—balanced with privacy, retention, and cost.
· 6 min read
- Memory Systems for LLM Agents — Short-Term vs Long-Term Memory
Episodic buffers, summarization, retrieval-augmented memory, and persistence patterns for agents—separating conversation state from durable knowledge stores.
· 6 min read
- Model Context Protocol in Agent Systems
How MCP standardizes how hosts expose tools, resources, and prompts to models—reducing one-off integrations while keeping authorization and transport security in the host’s hands.
· 6 min read
- Agent Planning Architectures — ReAct, Plan-and-Execute, and Tree-of-Thoughts
How common reasoning-loop patterns structure multi-step LLM behavior, where each pattern helps, and what operational complexity each adds at inference time.
· 6 min read
- Multi-Agent Systems — Coordination, Conflict, and Arbitration
Agent roles, voting patterns, consensus-style workflows, and hierarchical orchestration for multi-agent LLM systems—where coordination overhead and failure modes dominate the design.
· 6 min read
- Building Agentic AI Systems with Tool-Using LLMs
Tool execution loops, separation of planning and execution, and structured reasoning cycles for agents—emphasizing boundaries, state, and observability over anthropomorphism.
· 6 min read
- Cost Optimization in LLM Applications
Token budgeting, semantic and exact caching, model routing tiers, and fallback strategies to control spend without turning the product into a smaller model glued to a spreadsheet of hacks.
· 6 min read
- Latency Optimization in LLM Inference Systems
Streaming, batching, KV cache reuse, speculative decoding, and inference tradeoffs—described qualitatively for architects integrating provider APIs or self-hosted stacks.
· 6 min read
- Hybrid AI Systems — Rules, LLM, and Deterministic Code
How production systems combine classical business logic, LLM reasoning, and deterministic code paths so automation stays auditable, testable, and bounded.
· 6 min read
- Function Calling Architectures in LLM Systems
Tool schemas, routing logic, multi-tool chains, and error recovery patterns for LLM-driven tool use—treating tools as side effects with permissions, timeouts, and idempotency.
· 6 min read
- Structured Output Enforcement in LLM APIs
JSON schemas, function-calling payloads, validation pipelines, and retry-with-feedback loops for machine-consumable model outputs—without assuming schema mode guarantees semantic correctness.
· 6 min read
- Prompt Engineering as a System Design Discipline
Prompt templates, structured prompts, dynamic injection, and versioned prompt systems—treating prompts as APIs with contracts, tests, and release processes instead of copy in a notebook.
· 6 min read
- Contextual Grounding and Hallucination Reduction in LLM Systems
How retrieval, verification loops, and constrained generation patterns reduce unsupported answers—without claiming any pipeline eliminates model confabulation entirely.
· 6 min read
- Context Window Engineering for LLM Systems
Token budgets, truncation, summarization layers, and context packing—how production teams fit prompts, tools, and RAG evidence into finite windows without silent information loss.
· 6 min read
- Retrieval Strategies in RAG — Dense, Sparse, and Hybrid Search
When embedding-based ANN search wins, when lexical BM25-style retrieval wins, and how hybrid fusion behaves at scale—without pretending one algorithm fits every corpus.
· 6 min read
- Architecture of Production-Grade RAG Systems
How chunking, embeddings, retrieval, reranking, grounding, and latency budgets fit together in retrieval-augmented generation systems that survive real traffic—not demos.
· 6 min read
- Building Streaming AI Interfaces with OpenAI APIs
How token streaming and partial response rendering improve perceived latency in conversational systems — and what it takes to ship a streaming UI that actually works in production.
· 9 min read
- Prompt Engineering as an Engineering Discipline in Production LLM Systems
How systematic iteration using evals, latency tracking, and user feedback improves LLM reliability beyond ad-hoc prompting — and what a real prompt engineering workflow looks like.
· 10 min read
- Multi-Agent Orchestration Using LangGraph
How directed-graph execution lets specialized LLM agents collaborate, branch on conditions, and converge into a final synthesized response — with state, retries, and human-in-the-loop built in.
· 9 min read
- Model Context Protocol (MCP) in Multi-Agent AI Systems
How MCP standardizes tool and context exchange between LLM agents and external systems, enabling structured orchestration without bespoke integrations per agent.
· 9 min read
- Building Production RAG Pipelines with LangChain
How retrieval-augmented generation combines vector search over embeddings with LLM context injection to ground responses in real data — and what it takes to run that in production.
· 9 min read