RAG-testing-notes.txt

I agree 100%. Now that you have both the Space and Local environments running the target model (v3.3) with the correct prompt formatting 
and achieving good baseline RAG results, focusing on reliability and edge cases is absolutely the most critical next step. 
Ensuring the bot gives accurate answers, correctly handles missing information, and adheres to all constraints is paramount.

Next Steps for Reliability & Edge Case Testing:

Systematic Testing: Create a list of test questions covering various scenarios:
Direct Fact Retrieval: Questions with answers clearly stated in the document (e.g., "What time is check-out?", "Is the pool heated?").
Information Not Present: Questions the document cannot answer (e.g., "Do you offer room service dinner?", 
"What's the price of a beer?", "Is there a gym?"). Verify it gives the correct "I don't have that information" response.
Boundary/Constraint Testing: Questions designed to test the negative constraints:
Asking to perform actions ("Can you book me a taxi to the airport?", "Can I reserve a specific room?").
Potentially ambiguous or slightly off-topic questions.
(Carefully) Test abusive language handling.
Synthesis: Questions requiring combining information from different parts of the document (if applicable).
Phrasing Variations: Ask the same core question using different wording.

Evaluate Responses: For each test question, evaluate the model's response based on:
Accuracy: Is the factual information correct according to the document?
Grounding: Does it stick only to the document, or does it hallucinate/use external knowledge?
Completeness: Does it provide the relevant information found in the document?
Constraint Adherence: Does it correctly refuse to perform actions or handle out-of-scope/abusive queries?
Fallback: Does it use the appropriate "I don't have that information" response when necessary?

Iterate on System Prompt (If Needed): Based on any failures or weaknesses observed during testing, we can revisit the system prompt. 
Even though the current one is working well, minor tweaks might improve robustness for edge cases. For example, explicitly adding the 
negative constraints (no bookings, abuse handling) might make it even less likely to fail those tests.
I'm ready to help you analyze the test results and refine the system prompt whenever you are. Let the edge case testing begin!




GPT conversation:
=======================================================================================================================================
1. What an LLM actually “understands”
Tokens, not grammar nodes.
The model takes Unicode text, splits it into sub-word tokens, and turns those into vectors. There is no secret symbolic language inside; 
everything—English, Greek, emoji, XML—ends up in the same continuous vector space.

Semantics come from training data.
A sequence like "<SYS>" only has meaning if, during training or fine-tuning, the model repeatedly saw that exact token 
(or embedding) in contexts that taught it how to behave when it appears. Absent that exposure, the token is just another rare word 
and provides less guidance, not more.

2. Where hallucinations really come from

Root cause	Typical remedy
● Missing or stale knowledge	Retrieval-augmented generation (RAG), tool calls, or an up-to-date model
● Ambiguous or underspecified task	Clear, scoped instructions; structured output formats (JSON schemas, XML)
● High decoding entropy	Lower temperature / top-p; use beam search + reranking; self-consistency checks
● No grounded verification	Post-generation validation (fact-checking APIs, SQL queries, unit tests, etc.)
Changing the encoding of your instructions doesn’t target any of these directly.

3. When a special symbol can help
Fine-tune with a control token.
If you own the weights, you can prepend a token like <FRONT_DESK_MODE> during fine-tuning. The token’s embedding then becomes a 
compressed summary of your policy. At inference, hallucination rates often do drop because the model was explicitly trained to follow that mode.

Tokenizer tricks for structured output.
Defining single tokens for characters like “{” or XML tags can make the model more likely to emit syntactically valid JSON/XML, 
which indirectly reduces hallucinations in downstream parsers.

Grammar-constrained decoding.
Tools such as OpenAI’s “JSON mode” or libraries like Outlines let you supply a formal grammar. 
The model is forced to generate only strings that parse, which prevents a whole class of hallucinations (e.g., inventing nonexistent keys).

4. Practical recommendations

If you can fine-tune	If you cannot fine-tune
• Create a control token that stands for the entire system prompt and include it in every training example.	• Keep using clear natural language.
• Add retrieval (RAG) so the model cites verifiable passages.
• Use structured output + schema/grammar enforcement.
• Teach the model to refuse or fallback when the required info is missing.	• Lower temperature and apply post-generation fact-checking.
• Evaluate hallucination rates on held-out sets before/after the change.	• Consider a two-step “draft + critique/correct” prompting pattern.
Bottom line
No built-in abstract language exists that will magically solve hallucinations.

A learned control token can help, but only with extra training.

For off-the-shelf closed models, focus on retrieval grounding, unambiguous instructions, constrained decoding, 
and external validation—those give the largest, proven reductions in hallucination.

If you’d like concrete pointers to RAG frameworks, grammar-constrained decoding libraries, or a walkthrough of training 
a control token on an open-source model, just let me know!







You said:
I understand you.  I need to focus on the prompt in English and make it precise and clear in that language.  
Perhaps I should provide the system prompt to the model for review and iterate over it with the model to come up with the most precise, 
concise and logical system prompt. Do you think this is a good idea?
ChatGPT said:
Absolutely—that’s one of the most effective (and low-cost) ways to tighten a prompt. A few tips that make “prompt-in-the-loop” 
refinement pay off:

1. Treat the model as your drafting partner
Start with goals, not wording.
Before you ask the model to rewrite, state what you want the prompt to accomplish (e.g., “must only cite the provided documents, 
refuse if unsure, adopt a front-desk tone”).

Iterate in cycles.

Cycle A – Rewrite: “Here’s the current prompt—please make it clearer/shorter while preserving intent.”

Cycle B – Critique: “List any ambiguities or places the instructions might conflict.”

Cycle C – Stress-test: “Given this prompt, how would you answer these edge-case queries?”
Repeat until both rewrite and critique phases produce no substantive edits.

Lock the wording.
When the prompt is final, keep a version-controlled copy so every call uses the exact text. Tiny changes can shift outcomes.

2. Use checklists to drive precision
Ask the model (or do it yourself) to verify that the prompt explicitly covers:


Checklist item	Example phrasing
Scope	“Only use the documents provided; do not rely on external knowledge.”
Tone & persona	“You are the professional, polite front-desk assistant at Frangiorgio Hotel.”
Refusal style	“If data is insufficient, reply: ‘I’m sorry, but the requested information isn’t in our records.’”
Formatting rules	“Answer in short paragraphs, no bullet lists.”
Fallback/edge-cases	“When dates are ambiguous, ask a clarifying question before answering.”
Turning those into explicit bullets the model can’t miss is often the single biggest hallucination reducer.

3. Validate with representative queries
Create a mini test set:

Straightforward → “What time is check-out?”

Borderline → “Who designed the hotel’s lobby artwork?” (likely unavailable)

Ambiguous → “Do you have rooms available next holiday?” (date unclear)

Run the prompt against these queries at different temperatures (e.g., 0 → 0.7) and verify it still follows the instructions 
exactly—especially the refusal pattern.

4. Guardrails you can add later
Even a perfect system prompt can slip under high entropy or novel inputs. Layered defenses help:


Technique	Benefit
Lower temperature or top-p	Less creative drift → fewer hallucinations
RAG / embeddings lookup	Supply the relevant doc chunk inline; model stays grounded
JSON/grammar-constrained decoding	Ensures structurally valid, on-policy output
Post-generation validator	Optional second pass that flags unsupported facts
Bottom line
Iterative, model-assisted editing is a good idea. Make each round explicit (rewrite → critique → stress-test), 
lock the wording, and then validate with a short test suite. Combined with cautious decoding settings or RAG, 
you’ll get a far more reliable assistant—without needing any secret “internal language.” 
If you’d like, I can walk through a concrete first iteration on your draft prompt right now.