Field Note
Field Note: Three Common Reasons AI Projects Stay Stuck in the Pilot
AI pilot project failure usually rests on the same three reasons: undefined criteria, thinning support, and integration debt. A 6-criteria scale-up gate.
9 minStrategy
Blog archive
Showing 97–120 of 540 articles, newest first.
Explainer
What Is a High-Risk AI System Under the EU AI Act?
What is a high-risk AI system under the EU AI Act? Risk classes, the Annex III list, conformity assessment, and the obligations that follow, explained with a table and FAQ.
6 minGovernance
Article
Setting Up an LLM Evaluation Set: Designing the Golden Question Set
A guide to setting up an eval set: designing the golden question set, scoring rubric, human evaluator agreement, acceptance threshold and regression testing for LLM evaluation.
9 minLLMs
Guide
Enterprise RAG: An Architecture, Setup and Measurement Guide
How is enterprise RAG built? Pipeline layers, document preparation, retrieval, generation and quality measurement; a technical guide to enterprise RAG architecture and setup.
12 minRAG
Article
Where Do Open Source Models Stand in Enterprise Use?
Where does an open source LLM stand in enterprise use? License terms, closed-model comparison, operational load, and in which scenario it makes sense.
6 minLLMs
Field Note
Field Note: The Enterprise AI Transformation Experience — Recurring Patterns
Enterprise AI transformation experience shows the same patterns regardless of sector: data, ownership, pilot-to-scale and measurement. Field observations and early warnings.
9 minStrategy
Explainer
What Is Prompt Engineering? Core Principles for Enterprise Use
What is prompt engineering? The deliberate design of the instruction that gets the output you want from a language model: role, task, context, constraint, and output format.
6 minPrompt
Article
Chunking Strategy in RAG: The Right Choice by Document Type
In RAG, chunking strategy is not one setting; it varies by document type. The right chunk size, overlap ratio and method for contracts, tables and manuals.
8 minRAG
Guide
Enterprise AI Strategy: From Vision to a Measurable Roadmap
How to build an enterprise AI strategy, where to start, and why most strategies are never executed? A layer-by-layer guide that turns vision into a measurable roadmap.
11 minStrategy
Explainer
What Is Data Quality? 6 Dimensions, Measurement, and AI Impact
What is data quality? Data quality is the sum of dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — that determine data's fitness for its intended use. This guide: a clear definition, the six quality dimensions, measurement metrics, the data cleaning process, the impact on AI and RAG projects, common mistakes, and FAQs.
11 minRAG
Explainer
What Is Data Governance? Components, KVKK, and AI
What is data governance? Data governance is the set of policies and processes that define the ownership, quality, security, and usage rules of data in an organization. This guide: a clear definition, the difference from data management, core components, the KVKK dimension, its role in AI projects, implementation steps, and FAQs.
12 minGovernance
Comparison
SFT, DPO and RFT: Which Fine-Tuning Method to Choose in 2026?
I explain from the field the differences between SFT, DPO and RFT and when to use each: the fine-tuning decision from demonstrations to rewards, with KVKK notes.
18 minLLMs
Deep Dive
RAG Evaluation: Faithfulness, Context Precision and Recall with RAGAS
The four core metrics for measuring RAG systems: faithfulness, answer relevancy, context precision and recall. Evaluation with RAGAS, thresholds and context trust.
19 minRAG
Comparison
August 2026 Model Comparison: Claude Opus 5, GPT-5.6, Gemini 3.6, DeepSeek V4
I compare the leading LLMs as of August 2026 through an enterprise buyer's eyes: capability, cost, latency and KVKK data residency, with practical picks.
18 minLLMs
Guide
Meta-Prompting and Automatic Prompt Optimization: A DSPy Guide
Moving beyond brittle hand-written prompts: a practical guide to meta-prompting and metric-driven, programmatic prompt optimization with DSPy.
18 minPrompt
Deep Dive
LLM Cost Optimization: Token Economics, Prompt Caching and Semantic Cache
The strongest levers to control LLM cost in production: token economics, prompt caching, semantic cache and model routing, illustrated with 2026 pricing moves.
18 minLLMOps
Deep Dive
Hybrid Search: Combining BM25 and Vector Search in RAG
Pure vector search misses exact terms; pure keyword search misses meaning. A practical guide to combining BM25 and vector search in RAG with RRF and contextual retrieval.
18 minRAG
Deep Dive
EU AI Act GPAI Enforcement Is Live: What Changed for Turkish Companies on 2 August 2026
The European Commission's supervision and enforcement powers over GPAI providers took effect on 2 August 2026. A practical roadmap for Turkish companies plus the KVKK link.
16 minGovernance
Deep Dive
Memory in AI Agents: Short-Term and Long-Term Memory Architectures
Stateless LLM calls aren't enough for agents. Short/long-term memory, episodic-semantic-procedural memory, and practical architecture in light of KVKK's Agentic AI guideline.
18 minAgents
Deep Dive
RAG Evaluation in 2026: RAGAS, Faithfulness, OpenTelemetry, and Cost-per-Successful-Output
Building RAG is easy, proving it reliable is hard. Retrieval/generation metrics, reference-free evaluation with RAGAS, OpenTelemetry spans, and cost-per-successful-output.
15 minRAG
Deep Dive
Programmatic Prompt Optimization in 2026: DSPy, Eval-Driven Workflow, and Context Engineering
Prompt engineering is now engineering, not art. Automated optimization with DSPy, an eval-driven workflow, structured output, prompt chaining, and Turkish-specific evaluation.
15 minPrompt
Deep Dive
LLM Inference Cost and FinOps in 2026: Cost-per-Successful-Output, Not per Token
Inference is now 55-80% of AI cost. Model routing, caching, quantization, and the metric that matters: cost-per-successful-output. An LLM FinOps framework and the Turkey FX context.
14 minLLMOps
Deep Dive
Autonomy Levels and an ROI Framework for Enterprise AI Agents in 2026
The value of agentic AI is not in intelligence but in managing autonomy with discipline. A five-level autonomy ladder, ROI-vs-risk balance, human-in-the-loop thresholds, and a CTO/CDO evaluation framework.
15 minAgents
Comparison
Frontier Model Comparison, August 2026: GPT-5.x, Claude, and Gemini 3.x
The August 2026 frontier landscape: neck-and-neck on SWE-bench Verified, separation on SWE-bench Pro. Which model for which job, benchmark literacy, and the Turkish-performance criterion.
15 minLLMs