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To make a local language model recommend games you will actually enjoy, give it a structured, revisable player profile and make it follow a staged process: filter hard requirements, score the survivors against explicit preferences, explain every score from catalog data, then run a separate critique pass. A single paragraph such as “I like RPGs” leaves the model to guess about platform, budget, session length, mechanics and deal-breakers.
This approach combines conversational recommender principles—preference understanding, dialogue management and explainable results—with instruction-based recommendation, where an open model executes natural-language constraints. It also works well with a local runtime because the profile, catalog and output format can be controlled in your own application.
Why local game recommendations become generic
A model can produce fluent suggestions without understanding what makes a game suitable for a particular player. Generic results usually come from one or more of these gaps:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Missing hard constraints: the prompt does not say whether a game must run on a particular platform, support a controller, stay below a price ceiling or work offline.
- Unweighted tastes: a list of favorite genres does not tell the model whether mechanics, pacing, story, art direction or social play matters most.
- No negative preferences: the model keeps suggesting features the player actively dislikes, such as crafting, stealth, jump scares, card combat or always-online requirements.
- Unbounded catalog knowledge: the model fills missing metadata from memory, which can produce incorrect platform, price, multiplayer or content claims.
- One-shot interaction: when a player rejects a recommendation, the reason is lost, so the same mistake can recur.
Research on conversational recommender systems treats preference understanding, dialogue management and explainable recommendations as separate responsibilities. Instruction-based recommendation research shows how natural-language needs can be turned into instructions an open model executes. Your prompt should therefore behave more like a small decision procedure than a request for a list.
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Build a player profile the model can reason about
Store preferences as explicit fields instead of one permanent paragraph. A structured profile lets your application update a single preference, retain the reason for the update and distinguish what the player said from what the model inferred.
Hard constraints: reject violations first
Hard constraints are non-negotiable. A candidate that fails one should be removed before it receives a taste score.
- Platform and edition, such as Windows, Linux, console generation or handheld.
- Input method, including controller-only, keyboard and mouse, touch or accessibility hardware.
- Price ceiling and whether the limit applies to the base game, a subscription catalogue or downloadable content.
- Age and content limits, including violence, horror, sexual content, gambling mechanics or language.
- Accessibility requirements, such as remappable controls, subtitles, color options or difficulty settings.
- Solo, local co-op, online co-op or competitive multiplayer requirements.
- Online, offline or always-online operation.
Soft preferences: rank what remains
Soft preferences describe fit rather than eligibility. Record genres alongside the qualities that make them appealing: combat style, exploration, puzzle density, progression, camera, pacing, narrative structure, art direction and difficulty. Give a preference an optional weight or confidence value when the player has clearly stated that it matters more than another preference.
Negative preferences: make exclusions visible
Keep a separate list for disliked mechanics, themes and business models. “No roguelikes” is different from “I have not played a roguelike”; the former is an exclusion, while the latter is an opportunity for novelty. Include disliked monetization patterns, mandatory stealth, timers, permadeath, competitive matchmaking and always-online requirements when relevant.
Current context: recommendations change with the situation
A stable profile cannot express why the player wants a game tonight. Add available minutes, mood, whether friends are online, backlog goals and the desired balance between novelty and familiarity. A player with 25 minutes available needs a different recommendation from the same player planning a 40-hour campaign.
Evidence, confidence and time
For every important preference, retain whether it was stated directly or inferred, a confidence value and a last-updated timestamp. When a player says, “I rejected that because the missions were too long,” store the reason against session length rather than merely deleting the title. This makes the profile revisable instead of treating the first prompt as permanent truth.
Example profile schema
{
"player_id": "local-user-01",
"hard_constraints": {
"platforms": ["PC"],
"input": ["controller"],
"max_price": 25,
"currency": "USD",
"mode": ["solo"],
"network": "offline-capable",
"content_limits": ["no-gambling-mechanics"]
},
"soft_preferences": [
{"field":"mechanics","value":"exploration and tactical combat","weight":5,"confidence":0.9,"evidence":"stated","updated":"2026-10-19"},
{"field":"pacing","value":"short missions","weight":4,"confidence":0.8,"evidence":"inferred from rejection","updated":"2026-10-19"}
],
"negative_preferences": [
{"field":"mechanics","value":"permadeath","confidence":1.0,"evidence":"stated","updated":"2026-10-19"}
],
"context": {"minutes_available":45,"mood":"focused","novelty":"open to unfamiliar genres"},
"rejections": []
}Use your catalog’s actual field names. If a value is unknown, keep it unknown rather than inserting a plausible guess.
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Use a four-part prompt instead of one request
A reliable prompt separates policy, data, task and verification. The model then has fewer opportunities to confuse a preference with a requirement or invent a catalog fact.
1. System message: define the boundaries
You are a game recommender. Use only the supplied catalog and player profile. Separate stated preferences from inferences. If a fact is missing, say unknown. Never treat an inferred preference as a hard constraint.The system message should also state the permitted output format and whether the application will reject malformed responses. Keep it stable across requests so evaluations compare the recommendation method rather than changing instructions.
2. Profile block: provide typed data
Send the structured profile as JSON, including confidence and evidence for important preferences. Do not flatten it into prose. Typed fields make it possible for your application to validate that a price, platform or mode was actually supplied.
3. Candidate task: filter, score and explain
Remove candidates that violate hard constraints. Score the remainder from 0 to 5 for mechanics, pacing, theme, session length, platform fit and social mode. Return the top five with a score table, one catalog-grounded reason per score, key trade-offs and an uncertainty label. Ask one clarifying question only when its answer could change the ranking.The phrase “remove candidates” matters: asking for a score alone lets a highly appealing game survive a platform or accessibility violation. Requiring one reason per score makes unsupported enthusiasm easier to detect.
4. Critique pass: check the shortlist independently
Check every pick against every hard constraint and negative preference. Flag unsupported metadata, duplicate recommendations and overconfident claims. Replace or downgrade any failing item.Run this as a second model call or a separate validation stage. It should receive the proposed results and the same profile and catalog, not a shortened summary that omits the constraints.
Choose a scoring model that exposes trade-offs
A 0–5 score is useful only when each dimension has a consistent meaning. Define the scale in the prompt or application:
| Score | Interpretation |
|---|---|
| 0 | Conflicts with the preference or the catalog provides no basis for a positive fit. |
| 1 | Weak fit; a substantial mismatch is evident. |
| 2 | Some compatible elements, but important compromises are required. |
| 3 | Acceptable fit with no decisive advantage. |
| 4 | Strong fit supported by catalog details. |
| 5 | Exceptional fit, with several directly supported matching features. |
Keep hard constraints outside the score. A game should not receive a high platform score merely because it is desirable on other dimensions when it does not run on the player’s platform. Ask for trade-offs explicitly: a recommendation may score highly for mechanics but poorly for session length, and that difference is more useful than a single blended number.
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Ground recommendations in a controlled catalog
Give the model a fixed catalog snapshot containing the fields it is allowed to cite. Useful fields include title, supported platforms, price and price date, input support, modes, network requirement, accessibility features, content descriptors, mechanics, typical session structure, genre, art direction and source identifiers.
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- Represent missing values as
unknown, not as empty prose that invites guessing. - Pass only the candidates needed for the current task when the catalog is large.
- Require each explanation to name a field from the supplied record.
- Store the catalog version with the recommendation so a later price or platform change can be traced.
If a player asks for current prices, your application must refresh those values before prompting. A local model cannot make an unknown catalog field current by sounding certain.
Implement the workflow with Ollama
Ollama’s FAQ states: “Ollama runs locally. We don’t see your prompts or data when you run locally.” Local execution is useful when player profiles or private play history should remain on the device, but your application still needs to protect local files, logs and network endpoints.
Configure reusable instructions and context
Ollama supports configurable system messages and templates, conversation context and schema-constrained JSON output through its API. Put the stable system instruction in the model or request configuration, then send the profile, catalog slice and current task as the user content. Preserve conversation context when asking a clarification question so the model does not lose the original constraints. The API reference is at Ollama’s API documentation.
Request structured output
Define fields such as title, scores, reasons, trade-offs and uncertainty in a JSON schema. Validate the response before displaying it. A minimal response shape can look like this:
{
"recommendations": [
{
"title": "catalog title",
"scores": {
"mechanics": 0,
"pacing": 0,
"theme": 0,
"session_length": 0,
"platform_fit": 0,
"social_mode": 0
},
"reasons": [],
"trade_offs": [],
"uncertainty": "low|medium|high"
}
],
"clarifying_question": null
}Reject non-integer scores outside 0–5, titles absent from the supplied catalog and explanations that lack a corresponding catalog field. If the model returns malformed JSON, retry with the same data and a short formatting correction rather than silently parsing free text.
Keep deployment variables under version control
Record the model name, quantization, context length, prompt template, catalog version, profile schema and evaluation set. Without these values, a later change in latency or recommendation quality cannot be attributed to the model, the prompt or the data.
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Evaluate whether the new prompt is actually better
Do not describe the workflow as improving recommendations until you compare it with a baseline. Create a fixed catalog snapshot and held-out player profiles. Run at least two conditions:
| Condition | What it contains | What it tests |
|---|---|---|
| Simple baseline | A concise natural-language request with the same catalog. | How much the structured process changes results. |
| Structured profile | Typed hard, soft, negative and contextual fields. | Whether explicit preference coverage improves. |
| Structured plus critique | The profile and staged filter, score and verification passes. | Whether a second check reduces violations and unsupported claims. |
Where labeled relevant items exist, report hit rate, precision at k, recall at k and nDCG. Also report measures that ordinary ranking metrics miss:
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- Catalog-grounding errors: claims that are absent from or contradicted by the supplied record.
- Diversity and novelty: whether the list repeats familiar genres and titles or offers suitable alternatives.
- Explanation usefulness: evaluator ratings of whether each reason helps the player decide.
- Latency: median and p95 response time, including the critique pass.
Have evaluators mark whether every reason is supported by a catalog field. Keep the evaluator instructions and held-out profiles unchanged between prompt versions. A larger hit rate is not a win if it comes with more platform or catalog-grounding errors.
Handle clarifying questions without creating an interrogation
The model should ask one question only when the answer could change the ranking. Good questions resolve an unresolved hard constraint or a high-impact trade-off: “Must the game work fully offline?” or “Are 20-minute sessions a requirement or merely preferred?” Avoid asking about fields that cannot affect the current candidates. If no question would change the top five, return the recommendations and label uncertainty instead.
When the player answers, update the profile with the answer, evidence source and timestamp. Do not overwrite an explicit preference with an inference made from a single click.
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Ollama’s documentation notes that model loading depends on available system memory or VRAM. GPU inference and concurrent model loading also depend on whether models fit in the available GPU memory. A prompt change is not the first fix for a system that is swapping or repeatedly loading models.
- Check memory pressure: watch system RAM and GPU VRAM while loading the model and sending the longest profile and catalog context.
- Reduce context length: remove catalog fields that cannot affect the current decision and summarize old conversation turns into validated profile fields.
- Limit concurrency: queue requests if simultaneous model loads exceed available memory.
- Inspect thermals, drivers and background processes: throttling or contention can increase latency without any recommendation change.
- Record the configuration: model, quantization, context length and concurrency are needed to reproduce the measurement.
Do not assume that a more powerful graphics card is universally correct. The required VRAM depends on model size, quantization, context length and concurrency; choose hardware only after those variables are defined.
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Compare recommendation designs before choosing one
| Design | Preference coverage | Constraint adherence | Grounding and explanations | Latency and memory | Reproducibility |
|---|---|---|---|---|---|
| One-shot free-form prompt | Low to uneven; important fields are easy to omit. | Unreliable unless constraints are repeated clearly. | May be persuasive without catalog support. | Usually lowest request overhead. | Low because wording and conversation state vary. |
| Structured profile with staged scoring | High when fields are populated. | Filtering is explicit and testable. | Score reasons can be tied to catalog fields. | More prompt tokens; predictable for a fixed catalog slice. | Higher because schema and stages are versioned. |
| Structured profile plus critique | High, with a second check for omissions. | Best protection against violations among these designs. | Unsupported metadata and overconfidence can be flagged. | Highest latency and compute use. | Highest when both calls and their inputs are logged. |
These are design expectations, not benchmark results. Measure them on your catalog and held-out profiles before selecting a default.
Common failure modes and fixes
The model recommends a game outside the budget
Make price a hard field with currency and a price timestamp. Filter before scoring, and reject any candidate whose price is unknown when the player requires a verified ceiling.
The explanation sounds right but is not in the catalog
Require a reason for each score and validate that the cited attribute exists in the candidate record. Mark the result as uncertain or remove it when the field is missing.
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Record the rejection reason, not just the title. Update the relevant negative preference or pacing/session field with evidence and time, then include that record in the next profile block.
Every recommendation is from the same familiar genre
Add novelty as a current-context value and measure diversity separately from relevance. Ask for one or two alternatives that satisfy the hard constraints while differing on genre or mechanics, and label the uncertainty of those less familiar matches.
Long profiles make responses slow
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Quick Recap
A practical operating checklist
- Define hard, soft, negative and contextual fields before writing the prompt.
- Attach evidence, confidence and last-updated time to important preferences.
- Keep a versioned catalog snapshot with explicit unknown values.
- Use a stable system message that forbids unsupported facts.
- Filter hard constraints before scoring soft fit.
- Return per-dimension scores, catalog-grounded reasons, trade-offs and uncertainty.
- Run a critique pass for constraints, duplicates and unsupported metadata.
- Ask only clarifying questions that can change the ranking.
- Validate schema-constrained JSON before displaying results.
- Compare against a simple baseline using ranking, violation, grounding, diversity, explanation and latency measures.
- Version the model, quantization, context length, template, catalog, profile schema and evaluation set.
- Monitor RAM, VRAM, thermals, drivers and background processes when inference slows.
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