Tag

#production ai

10 posts

The Context Engineering Era: Prompt Caching, Long Context vs RAG, and Runtime State Management (2026 Guide)

Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.

40 min

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yapay-zeka

Replace Classic RAG with Agentic RAG in 2026: Production Architecture on LangGraph

Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.

38 min

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yapay-zeka

Few-Shot Learning Prompt Optimization 2026: Deep Turkish Technical Guide — From GPT-3 to Modern LLMs

Most comprehensive Turkish technical guide for Few-Shot Learning prompt optimization: academic origins (Brown et al. 2020 GPT-3 paper, in-context learning discovery), 8 example selection strategies (random, similarity-based KATE, diversity, semantic, active learning), optimum example count analysis (1 vs 3 vs 5 vs 10 vs 32), ordering effects (Lu et al. 2022 'lost in middle'), delimiter and formatting best practices, Anthropic XML tags pattern, Few-Shot + CoT combination, recency + primacy bias, dynamic few-shot retrieval, prompt versioning, A/B test framework, 25+ Turkish practical examples, evaluation framework, production deployment.

22 min

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blog-ai-is-stratejisi-ve-kurumsal-donusum

Why Calling the Most Expensive LLM for Every Task Is the Wrong Strategy: A Guide to Cost, Quality, and Model Routing

Many companies begin their generative AI journey by choosing the safest-looking option: using the largest and most expensive LLM for nearly every task. At first, this seems reasonable. If the most capable model is used everywhere, output quality should stay high. But production reality is usually different. Not every task requires the same reasoning depth, context window, or model capacity. Using the most expensive model for simple classification, summarization, extraction, rewriting, template filling, or low-risk workflow steps can dramatically increase cost without improving quality proportionally. In some cases, it even creates more latency, more inconsistency, and a weaker ROI story. That is why enterprise LLM design is not about putting the strongest model everywhere. It is about identifying which task truly needs which level of capability, building routing logic, decomposing workflows, adding evaluation and guardrails, and optimizing around cost per successful task. This guide explains why calling the most expensive LLM for every job is the wrong strategy, covering cost structure, quality illusions, task-model fit, routing architectures, prompt and context optimization, hybrid inference strategies, observability, evaluation, and enterprise AI economics.

35 min

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blog-bilgisayarli-goru

How to Manage Data Quality, Domain Shift, and Real-World Performance in Vision Systems

High benchmark accuracy in vision systems is not enough to guarantee reliable real-world behavior. A model may perform strongly in controlled evaluation settings yet degrade significantly in production due to camera variation, lighting changes, background diversity, label quality issues, class imbalance, rare scenarios, device differences, seasonal changes, and workflow drift. That is why modern computer vision projects are not only about model architecture. They require strong data quality management, domain shift analysis, slice-based evaluation, error-cost awareness, production monitoring, and continuous improvement loops. This guide explains how to manage data quality, diagnose domain shift, measure real-world performance, and build robust vision systems that remain reliable beyond the lab.

31 min

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blog-derin-ogrenme

Overfitting, Underfitting, and Generalization: How Real Performance Is Built in Deep Learning

One of the most misunderstood topics in deep learning is the assumption that training success and real performance are the same thing. In reality, low training error, strong validation metrics, or short-term impressive outputs do not always mean that a model generalizes well, behaves reliably, or remains robust in the real world. Overfitting happens when a model adapts too strongly to dataset-specific noise and patterns instead of learning the underlying structure. Underfitting happens when the model fails to capture even the core structure of the problem. Generalization is the model’s ability to perform consistently on unseen data. This guide explains overfitting, underfitting, and generalization not only conceptually, but through the lenses of data, model capacity, regularization, evaluation, training dynamics, and production AI.

30 min

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blog-derin-ogrenme

From Training to Production in Deep Learning Projects: A Model Alone Is Not Enough

One of the most common mistakes in deep learning projects is assuming that a model with strong training metrics is ready for production. In reality, high accuracy, low loss, or strong validation performance do not guarantee readiness under real user traffic, distribution shift, latency constraints, security requirements, observability needs, failure handling, version control, or operational sustainability. Production success depends not only on model architecture, but also on data pipelines, inference design, model packaging, serving infrastructure, monitoring, rollback strategy, evaluation discipline, governance, and workflow integration. This guide explains why moving from training to production in deep learning projects requires much more than a good model, and what a production-grade AI system actually needs.

29 min

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blog-mlops-llmops-ai-engineering

Comparing the AI Engineering Stack: Orchestration, Deployment, Observability, and Evaluation Layers

Production-grade AI systems require far more than choosing a model or framework. Real success depends on how well orchestration, deployment, observability, evaluation, security, and governance layers work together. This guide compares the core layers of the AI engineering stack, explains what each layer is responsible for, where teams make the wrong architectural decisions, and how organizations can build a more reliable and scalable AI operating model.

21 min

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blog-mlops-llmops-ai-engineering

Model Monitoring, Drift, and Feedback Loop Design: How AI Systems Survive in Production

Deploying an AI model is not the finish line. In production, even high-performing models can degrade silently due to data drift, concept drift, delayed labels, segment-level failures, and weak feedback loop design. This guide explains how to build a production-grade monitoring strategy, how to detect and interpret drift correctly, and how to design feedback loops that keep AI systems reliable, measurable, and continuously improving over time.

20 min

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blog-mlops-llmops-ai-engineering

From PoC to Production: The 12 Most Common Architectural Mistakes in AI Engineering

Many AI projects start with an impressive proof of concept but fail when they move toward production. In most cases, the root cause is not model quality alone, but weak architectural decisions, missing operational discipline, and a lack of production-grade AI engineering practices. This guide explains the 12 most common architecture mistakes teams make on the journey from PoC to production, and shows how to build more reliable, scalable, and maintainable AI systems.

20 min