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A local LLM recommends games you will actually enjoy when you give it a structured, revisable player profile, a catalog it is allowed to use, and a prompt that separates filtering, scoring, explanation, and verification. Put platform, price, session length, mechanics, accessibility, dislikes, current mood, and confidence in explicit fields; then make the model prove every pick against those fields. Running the model locally keeps prompts and profile data on your machine: the Ollama FAQ states, “Ollama runs locally. We don’t see your prompts or data when you run locally.” Ollama FAQ
Why a local model gives generic game suggestions
A language model can produce fluent recommendations without understanding what would make a game workable for a particular player. A short request such as “recommend an RPG” leaves platform, price, input method, age rating, online requirements, session length, difficulty, and disliked mechanics unspecified. The model then fills gaps with stereotypes or whatever titles are most familiar in its training data.
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Conversational-recommender research identifies three capabilities that matter: understanding preferences, managing the dialogue, and explaining recommendations. Research on leveraging large language models in conversational recommender systems describes these as connected tasks rather than a single act of naming products. Instruction-following recommendation research likewise treats a user’s needs as natural-language instructions that an open model can execute. Recommendation as Instruction Following
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- Profile: explicit constraints, preferences, dislikes, current context, and evidence quality.
- Catalog: the only source from which the model may select titles and facts.
- Prompt stages: filter first, score second, explain third, and audit last.
- Evaluation: a fixed test set that shows whether the changes help instead of merely sounding better.
These layers reduce hallucinated facts and make a recommendation auditable. They do not guarantee that every suggestion will be a hit; the model still depends on the completeness and accuracy of your catalog and profile.
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Build a player profile the model can reason about
Store the profile as fields rather than one permanent paragraph. A paragraph hides which details are mandatory, which are preferences, and which were guesses. Fields also let you update one belief when a player rejects a recommendation.
Hard constraints
Hard constraints eliminate a game before any taste score is calculated. Typical fields include:
- Platforms and operating systems.
- Input method, such as controller, mouse and keyboard, or accessibility hardware.
- Maximum price and whether subscription access is acceptable.
- Age-rating or content limits.
- Accessibility requirements, including remappable controls, subtitle needs, color-vision considerations, or motion sensitivity.
- Solo, local co-op, online co-op, or competitive multiplayer mode.
- Offline requirement, maximum download size, or always-online prohibition.
If a hard constraint is unknown, mark it as unknown. Do not silently convert an absence of data into permission.
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Soft preferences influence ranking among eligible games. Record genres, mechanics, pacing, camera perspective, difficulty, narrative style, art direction, exploration density, and preferred session length. Use values such as “likes,” “neutral,” and “avoids,” or a small numeric scale, but keep the meaning of the scale consistent.
Negative preferences
Dislikes deserve their own list because a model can otherwise overvalue a broad genre match. Capture disliked mechanics, themes, monetization patterns, forced stealth, permadeath, competitive ranking, jump scares, or other specific reasons a player quits. A negative preference can be a hard veto or a soft penalty; label which one it is.
Current context
The same person can want different games on different days. Record available minutes, mood, whether friends are online, backlog goals, and whether the player wants novelty or familiarity. “I have 25 minutes before bed” should affect the ranking differently from “I want a 60-hour project for a holiday.”
Evidence, confidence, and time
For every preference, retain whether it was stated directly or inferred, a confidence value, and the last-updated timestamp. For example, “dislikes crafting” stated directly after a rejected game is stronger evidence than an inference from two skipped titles. Keeping the reason and time makes the profile revisable instead of treating the first prompt as permanent truth.
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Example profile object
{"hard_constraints":{"platforms":["PC"],"input":["controller"],"max_price":25,"mode":["solo"],"offline_required":true},"soft_preferences":{"genres":["immersive sim","strategy"],"mechanics":["systems-driven choices","short runs"],"pacing":"deliberate","camera":"first-person or isometric"},"negative_preferences":[{"item":"always-online","strength":"veto"},{"item":"loot boxes","strength":"strong_penalty"}],"current_context":{"minutes_available":45,"mood":"focused","novelty":"high"},"evidence":[{"field":"mechanics.systems-driven choices","source":"stated","confidence":0.9,"updated":"2026-10-19"}]}The exact field names are yours to choose. Consistency matters more than a particular schema, because the prompt and evaluation scripts must interpret the fields the same way.
Give the model a trustworthy catalog
Recommendations can only be as accurate as the game records supplied at inference time. Create a catalog snapshot with one record per game and fields such as title, supported platform, input support, price and currency with an “as of” date, age rating, content notes, play modes, offline behavior, typical session length, mechanics, pacing, accessibility features, and monetization.
Keep unknown values explicit. If the catalog does not establish whether a title works offline, the model should say “unknown” rather than infer it from genre or reputation. Include a catalog version or date in every request so a later price or platform change can be traced.
Tell the model to use only the supplied catalog. This prevents it from inventing a release, feature, or price and makes explanations checkable. A catalog-grounded reason should point to a field such as “controller support” or “20–40 minute sessions,” not to an unsupported claim about what the game feels like.
Use a staged prompt instead of one giant question
Separate the model’s jobs. A system message establishes the rules; a profile block supplies structured evidence; a candidate task filters and scores; a critique pass checks the result. This mirrors the preference-understanding, dialogue, and explanation roles identified in conversational-recommender work.
1. System message
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 relax a hard constraint to fill a quota.This instruction prevents the common failure in which a fluent model silently substitutes its own knowledge for the catalog.
2. Profile and catalog blocks
Send the structured profile as JSON and label each preference’s evidence and confidence. Send the catalog snapshot in the same request or through your application’s context mechanism. Keep the schema stable so your evaluator can compare outputs across model versions.
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3. Candidate task
Remove every candidate that violates a hard constraint. Score each remaining game from 0 to 5 for mechanics, pacing, theme, session length, platform fit, and social mode. Return the top five in rank order with a score table, one catalog-grounded reason for every score, key trade-offs, and an uncertainty label. Ask one clarifying question only when its answer could change the ranking.Filtering before scoring matters. A high mechanics score must not rescue a game on an unsupported platform or one that requires an always-online connection when offline play is mandatory.
4. Critique pass
Check every pick against every hard constraint and negative preference. Flag unsupported metadata, duplicate recommendations, and overconfident claims. Replace or downgrade any failing item. Preserve unknown values instead of guessing.Run this as a second model call or a separate validation step in your application. It should receive the proposed list and the same profile and catalog, not a vague request to “improve” the prose.
Make the output machine-checkable with Ollama
Ollama supports configurable system messages and templates, conversation context, and schema-constrained JSON output through its API. See the Ollama API documentation for the current request and response fields. Use a JSON schema that requires stable keys such as title, scores, reasons, trade-offs, and uncertainty.
A practical response shape is:
{"recommendations":[{"title":"catalog title","scores":{"mechanics":0,"pacing":0,"theme":0,"session_length":0,"platform_fit":0,"social_mode":0},"reasons":["catalog field and preference match"],"tradeoffs":["known compromise"],"uncertainty":"low|medium|high"}],"clarifying_question":null}Validate the returned JSON before displaying it. Reject missing titles, scores outside 0–5, reasons that do not cite catalog fields, and a clarifying question when the ranking is already determined. Structured output does not make an unsupported fact true; it only makes the shape predictable.
Ask clarifying questions only when they change the result
Questions have a cost: they interrupt the player and add another turn. Ask one when two or more candidates are separated by an unresolved hard or high-impact soft preference. Examples include “Is local co-op required, or merely welcome?” and “Is a 30-minute session limit strict today?”
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Compare recommendation designs before claiming improvement
Use the same catalog snapshot and held-out player profiles for every variant. A useful comparison is:
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| Design | Preference coverage | Constraint adherence | Grounding and explanations | Privacy and reproducibility | Latency and memory |
|---|---|---|---|---|---|
| One-shot natural-language prompt | Often incomplete; depends on what the player remembers to mention | Not guaranteed; filtering and ranking are mixed | May be fluent but unsupported | Local privacy is possible; profile format is hard to version | Usually one call, but context still consumes memory |
| Structured profile plus staged prompt | Explicit hard, soft, negative, and contextual fields | Filter stage and critique stage make violations visible | Requires one catalog-grounded reason per score | Schema and prompt can be versioned locally | More calls and a longer context; memory demand rises with context |
| Structured profile plus staged prompt and evaluation set | Coverage is measured on held-out profiles | Violations are counted rather than assumed away | Evaluators mark whether each reason is supported | Catalog, profile schema, prompts, and test set are reproducible | Evaluation adds batch time and storage, not a claim of better quality by itself |
The table describes design properties, not measured wins. Do not say a prompt improved recommendations until the comparison has actually been run.
Evaluate whether recommendations are genuinely better
Create a fixed test set
Freeze a catalog snapshot and create held-out profiles that cover different platforms, budgets, session lengths, social modes, accessibility requirements, and novelty preferences. Keep some profiles with unknown fields to test whether the model admits uncertainty.
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Where you have trusted labels or choices, report hit rate, precision@k, recall@k, and nDCG. Define k in advance, such as the top five requested by the prompt, and keep the same candidate pool for each prompt variant.
Measure safety and usefulness beyond hits
- Hard-constraint violations: count picks that fail platform, price, mode, accessibility, age, or offline requirements.
- Catalog-grounding errors: have evaluators mark whether each stated reason is supported by a catalog field.
- Diversity and novelty: track whether the list repeats the same familiar titles or offers viable alternatives.
- Explanation usefulness: ask evaluators whether trade-offs and uncertainty help them decide.
- Latency: record median and p95 response time, especially after adding a critique call.
Keep the evaluation prompts and model settings fixed while comparing variants. A model that sounds more persuasive but violates more constraints is not an improvement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run the local model reliably
Ollama documents that model loading depends on available system memory or VRAM. GPU inference and concurrent model loading can depend on whether the models fit entirely in available GPU memory. Its FAQ explains the memory and concurrency behavior. The right graphics card therefore depends on model size, quantization, context length, and the number of concurrent requests; there is no single card that fits every local setup.
Version the inputs that affect an answer
- Model name and quantization.
- System prompt and reusable template.
- Context length and generation settings.
- Catalog version and price-date metadata.
- Player-profile schema and migration rules.
- Evaluation profiles, labels, and scoring code.
Without these records, a later change in ranking may be impossible to reproduce.
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Diagnose slowness before rewriting the prompt
- Check available RAM and VRAM while a request is running.
- Reduce unnecessary catalog text and profile history, or lower context length, while preserving required fields.
- Check whether multiple requests are forcing model reloads or competing for GPU memory.
- Inspect thermals, graphics drivers, and background processes.
- Only after those checks, compare a shorter prompt or a smaller quantized model.
Changing the recommendation wording first can hide an infrastructure bottleneck. Record latency and memory observations alongside each evaluation run.
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Handle rejection as useful preference data
When a player rejects a recommendation, ask for the reason in a constrained way: wrong platform, too long, disliked mechanic, unsuitable mood, missing accessibility feature, price, or another catalog fact. Store the reason, whether it was stated or inferred, confidence, and timestamp. Then update only the affected field.
For example, rejecting one title because its combat is real-time should not erase a general preference for the title’s genre. A revisable profile preserves that distinction and prevents the system from overreacting to a single exception.
Common failure modes and fixes
The model recommends unavailable games
Cause: it is using prior knowledge instead of the supplied catalog, or the catalog lacks platform data. Fix: enforce catalog-only selection, mark missing fields unknown, and reject any title absent from the snapshot.
A hard requirement is treated as a suggestion
Cause: filtering and scoring occur in one step. Fix: put an explicit removal instruction before scoring and repeat the check in the critique pass.
Every explanation sounds positive
Cause: the prompt asks for reasons but not trade-offs. Fix: require one catalog-grounded reason, one compromise, and an uncertainty label for every pick.
The list is repetitive
Cause: the model optimizes familiarity or the catalog contains duplicate records. Fix: flag duplicate recommendations in the critique pass and evaluate diversity or novelty as a separate measure.
Profile changes do not affect rankings
Cause: stale conversation context, an unversioned profile, or a field name the prompt does not use. Fix: include the current profile version explicitly, inspect the rendered request, and test one changed field at a time.
Responses become slow after adding more history
Cause: longer context and memory pressure. Fix: summarize old interactions into dated evidence fields, trim duplicate catalog text, check concurrency, and monitor RAM, VRAM, thermals, drivers, and background processes.
A repeatable workflow
- Define the profile schema with hard, soft, negative, context, evidence, confidence, and timestamp fields.
- Normalize a dated catalog snapshot and mark unknown metadata explicitly.
- Write the system message that forbids unsupported facts and separates stated preferences from inferences.
- Send the profile and catalog, filter hard constraints, score the survivors, and request grounded reasons, trade-offs, and uncertainty.
- Run the critique pass and validate the JSON schema before showing results.
- Ask one targeted clarification only if its answer could change the ranking.
- Log the player’s acceptance or rejection and update the affected preference with its source and time.
- Evaluate prompt variants on the same held-out profiles using ranking, violation, grounding, diversity, explanation, and latency metrics.
- Version the model, quantization, template, context length, catalog, profile schema, and evaluation set so a change can be reproduced.
This process turns a local chatbot into a transparent recommendation tool: constraints determine eligibility, preferences determine order, catalog fields support explanations, and evaluation determines whether a change earned the word “better.”
Quick Recap
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