Category: Emerging Technologies

Introduction: Prompts are code—they define your application’s behavior and should be managed with the same rigor as source code. Yet many teams treat prompts as ad-hoc strings scattered throughout their codebase, making it impossible to track changes, compare versions, or systematically improve performance. This guide covers practical prompt management: version control systems for prompts, A/B… Continue reading

Introduction: Vector search finds semantically similar content, but it misses the structured relationships that make knowledge truly useful. Knowledge graphs capture entities and their relationships explicitly—who works where, what depends on what, how concepts connect. Combining knowledge graphs with LLMs creates systems that can reason over structured relationships while generating natural language responses. This guide… Continue reading

Introduction: Fine-tuning transforms general-purpose language models into specialized tools for your domain. While prompting works for many tasks, fine-tuning delivers consistent behavior, lower latency, and reduced token costs when you need the model to reliably follow specific formats, use domain terminology, or exhibit particular reasoning patterns. This guide covers practical fine-tuning strategies: preparing high-quality training… Continue reading

Introduction: Initial retrieval casts a wide net—vector search or keyword matching returns candidates that might be relevant. Reranking narrows the focus, using more expensive but accurate models to score each candidate against the query. Cross-encoders process query-document pairs together, capturing fine-grained semantic relationships that bi-encoders miss. This two-stage approach balances efficiency with accuracy: fast retrieval… Continue reading

Introduction: Long contexts contain valuable information, but they also contain noise, redundancy, and irrelevant details that consume tokens and dilute model attention. Context distillation extracts the essential information from lengthy documents, conversations, or retrieved passages, producing compact representations that preserve what matters while discarding what doesn’t. This technique is crucial for RAG systems processing multiple… Continue reading

Introduction: LLM inference is expensive—both in compute and latency. Every token generated requires a forward pass through billions of parameters, and users expect responses in seconds, not minutes. Inference optimization techniques reduce costs and improve responsiveness without sacrificing output quality. This guide covers practical optimization strategies: batching requests to maximize GPU utilization, managing KV caches… Continue reading

Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with… Continue reading

Introduction: Not every request needs GPT-4. Simple questions can be handled by smaller, faster, cheaper models, while complex reasoning tasks benefit from more capable ones. Model routing intelligently directs requests to the most appropriate model based on task complexity, cost constraints, latency requirements, and quality needs. This approach can reduce costs by 50-80% while maintaining… Continue reading

Introduction: LLMs are stateless—each request starts fresh with no memory of previous interactions. Building conversational applications requires implementing memory systems that maintain context across turns while staying within token limits. The challenge is balancing completeness (keeping all relevant context) with efficiency (not wasting tokens on irrelevant history). This guide covers practical memory patterns: buffer memory… Continue reading

Introduction: LLMs can generate harmful, biased, or inappropriate content. They can be manipulated through prompt injection, jailbreaks, and adversarial inputs. Production applications need guardrails—safety mechanisms that validate inputs, moderate content, and filter outputs before they reach users. This guide covers practical guardrail implementations: input validation to catch malicious prompts, content moderation using classifiers and LLM-based… Continue reading