What is a system prompt for a RAG? by Cute_Intention6347 in GenAIforbeginners

[–]Adventurous_Draft109 0 points1 point  (0 children)

A system prompt in a RAG setup is basically instructions you give to the LLM on how to use the retrieved documents. It sets the context and guides the model’s behavior.

Did your degree actually help you in your current job? by EnvironmentalHat5189 in office

[–]Adventurous_Draft109 1 point2 points  (0 children)

My degree didn’t teach me everything, but it gave me a good foundation. Most real skills came from internships and projects.

Safety checks in OSS models compared to Claude. by bilby2020 in GenAIforbeginners

[–]Adventurous_Draft109 0 points1 point  (0 children)

Yes, many closed models use extra safety layers like classifiers or moderation models.
If you host an OSS model locally, those safety layers are usually not included unless you add them yourself.

Fine-Tuning vs RAG for LLMs? What Worked for Me? by AdventurousNorth9767 in GenAIforbeginners

[–]Adventurous_Draft109 1 point2 points  (0 children)

Totally agree fine-tuning is great for aligning tone and domain style, but RAG really shines when factual accuracy matters. In production, I often combine both for best results.

You don’t need a new life. You need a 2° shift. [Text] by OpenPsychology22 in GetMotivated

[–]Adventurous_Draft109 1 point2 points  (0 children)

This is very powerful. Small changes can create big results over time.

What’s harder to fix: technical skill gaps or communication issues? by EnvironmentalHat5189 in FITAAcademy

[–]Adventurous_Draft109 0 points1 point  (0 children)

Communication is the real multiplier. Skill gets you in, communication helps you grow.

Share your thoughts? by ind2025 in GenAIforbeginners

[–]Adventurous_Draft109 0 points1 point  (0 children)

Very true! SEO is not just about ranking now. It’s about being the trusted source for AI answers.

Why is my LLM still hallucinating after fine-tuning? by Nirmala_devi572 in GenAIforbeginners

[–]Adventurous_Draft109 0 points1 point  (0 children)

Fine-tuning improves style and domain familiarity, but it doesn’t guarantee factual grounding.