DEV Community: Salik Ahmad

Why LLMs Hallucinate and How to Reduce Hallucinations

Salik Ahmad — Thu, 20 Aug 2026 16:56:17 +0000

Large Language Models (LLMs) have transformed how we search for information, write content, generate code, analyze documents, and build intelligent applications. However, despite their impressive capabilities, LLMs sometimes generate answers that sound convincing but are factually incorrect, unsupported, or completely fabricated. This behavior is commonly known as LLM hallucination.

One of the most important challenges in building reliable AI systems is understanding why hallucinations happen in the first place. An LLM does not inherently “know” whether every statement it produces is true. It generates text by predicting likely sequences based on patterns learned during training and the context provided at inference time. As a result, a model can produce a fluent and confident answer even when it does not have enough information to answer the question correctly.

LLM hallucinations can occur for several reasons. A model may have incomplete or outdated knowledge, receive insufficient context, misunderstand an ambiguous prompt, or generate a plausible answer when the required information is unavailable. The probabilistic nature of language generation also means that producing a fluent response does not guarantee that the underlying information is true. This is why an LLM can confidently provide a fabricated citation, invent a fact, or give an incorrect technical explanation while producing perfectly natural language.

Reducing hallucinations requires more than improving the prompt. Reliable LLM systems can combine clear prompt design, Retrieval-Augmented Generation (RAG), trusted external sources, tool and function calling, structured outputs, fine-tuning, response verification, and systematic evaluation. RAG can provide models with relevant information at generation time, while tools can allow them to retrieve current or precise information instead of relying entirely on learned knowledge. Evaluation and verification can then help identify cases where the generated response is unsupported or incorrect.

The goal is not to claim that hallucinations can be completely eliminated. Instead, the goal is to understand their underlying causes and learn how to design AI systems that are more accurate, grounded, verifiable, and reliable.

Understanding hallucinations is essential for anyone working with modern generative AI. The central challenge is not simply making an LLM produce more text, but making its answers trustworthy, grounded, and verifiable. By understanding why hallucinations occur and applying the right techniques at the application and model level, developers can significantly improve the reliability of LLM-powered systems.

What Happens Inside an LLM When You Ask a Question?

Salik Ahmad — Thu, 20 Aug 2026 16:54:46 +0000

When you send a question to a Large Language Model, the response may appear almost instant, but a complex sequence of processes happens behind the scenes before the answer reaches you. An LLM does not simply “look up” the answer like a traditional search engine. It processes your input, converts the text into tokens, represents those tokens as numerical vectors, and uses transformer layers and attention mechanisms to determine how different parts of the input relate to each other.

The process begins with tokenization, where your question is broken into smaller units that the model can process. These tokens are converted into numerical representations called embeddings and passed through the model’s transformer architecture. The self-attention mechanism allows the model to determine which tokens are important in relation to others and build a contextual representation of the prompt.

After processing the input, the model generates the response one token at a time. At each step, it calculates probabilities for possible next tokens and selects one according to its decoding strategy. The newly generated token is then added to the context, and the process continues until the response is complete.

This explains why concepts such as attention, embeddings, context windows, logits, probability distributions, temperature, and token generation are fundamental to understanding how modern LLMs work.

Understanding this process also helps explain many behaviors of LLMs, including why they can produce highly coherent answers, why changing a prompt can change the output, why longer contexts can affect performance, and why models can sometimes generate incorrect information with high confidence.

Rather than treating an LLM as a database that retrieves stored answers, it is more accurate to understand it as a neural network that has learned complex patterns from data and uses those patterns to predict what tokens are likely to come next given the current context. This perspective provides a foundation for understanding modern generative AI and the technologies built on top of LLMs.

Introducing Sentira CORE

Salik Ahmad — Fri, 27 Feb 2026 07:48:38 +0000

🚀 Introducing Sentira CORE | Neural Sentiment Engine 💠

Excited to share Sentira CORE, an interactive NLP & ML-powered tool that analyzes text emotions with high precision. Detects six emotions — Joy, Love, Surprise, Sadness, Anger, Fear — and provides real-time insights.

🧪 Algorithm: Linear Support Vector Classifier (LinearSVC)

🎯 Accuracy: ~89.2%

📐 Vectorizer: TF-IDF with custom NLTK stopword filtering

🏷️ Classes: Joy · Love · Surprise · Sadness · Anger · Fear

⚡ Inference: Real-time (< 100ms)

🎨 Features:

Neural sentiment processing in real-time

Emotional Spectrum Index: Positive vs Negative

Ultra-modern glassmorphism UI/UX

🔗 Try it live: https://lnkd.in/gEgzUv7k

📂 GitHub Repo: https://lnkd.in/gEk6vSVG

💬 I’d love your thoughts! How would you use sentiment analysis in your projects?

NLP #MachineLearning #AI #SentimentAnalysis #Python #Streamlit #DataScience #AIEngineering #Innovation