About Finster Chat

Models are trained on very large collections of text: books, articles, documentation, and other public sources. This data provides the statistical patterns the model learns (language, facts, typical reasoning). Care is taken to respect licenses and privacy when preparing datasets.

Raw text is tokenized (broken into numerical tokens) so the model learns to predict the next token given context. Higher-quality and domain-specific data improves performance for particular use cases like finance.

Most modern language models use Transformer architectures. Transformers learn relationships in text using self-attention and scale well to billions of parameters (weights) that encode knowledge learned during training.

Model capability is influenced by data quality, model size, and compute — larger models often generalize better but cost more to train and serve.

After training a base model, developers fine-tune it on higher-quality or domain-specific examples so the assistant behaves better for target tasks. Supervised fine-tuning and reinforcement learning from human feedback (RLHF) help align outputs with human preferences and safety goals.

Prompt design and safety layers further shape behavior so Finster gives clearer, more relevant finance guidance.

Models are evaluated for accuracy, helpfulness, and safety using automated tests and human review. Providers also apply content filters, monitoring, and rate limits to reduce harmful or misleading responses.

Despite safeguards, models can hallucinate or return outdated information — always verify important facts with trusted sources.

XAI techniques help developers understand why models produce particular outputs. Common techniques include feature attribution (which tokens influenced the result), example-based explanations (showing similar training examples), and model probing (testing knowledge of facts or rules).

XAI provides useful signals but does not make very large models fully transparent — interpretability remains an active research area.